Lifeotypes

ABSTRACT

The present invention provides a method and a system for assembling data from at least one data source into at least one life bit, assembling the at least one life bit into at least one life byte, and defining a lifeotype based on the attributes of at least one life byte.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of the following provisionalapplication, which is hereby incorporated by reference in its entirety:

Ser. No. 60/901,952, SYSTEMS AND METHODS FOR UNDERSTANDING AND APPLYINGTHE PHYSIOLOGICAL AND CONTEXTUAL LIFE PATTERNS OF AN INDIVIDUAL OR SETOF INDIVIDUALS, filed Feb. 16, 2007.

BACKGROUND

1. Field

The invention relates to the field of data informatics, and morespecifically to systems and methods for analyzing and parsinginformation relating to information monitored about subjects, includinghuman lifestyle information.

2. Description of the Related Art

Vast resources have been devoted to the sequencing of the human geneticcode and to cataloging the influence of genes and other physiologicaltraits. However, a major component of health and wellness can beattributed to the interactions of subjects with their environment,including their lifestyles. Despite the widely accepted view thatlifestyle activities, such as those related to diet, exercise, sleephabits and the like, affect health and wellness, efforts to catalogthose effects to date have been limited. A need exists for methods andsystems that systematically catalog the effects of various humanlifestyles on a wide range of outcomes; that is, a need exists tosequence the human lifestyle. The low cost and ready availability ofsensors has reduced costs of collecting data. In addition, improved dataintegration and processing methods have allowed for use of existing datasources. However, this wealth of data has not yet led to a betteroverall understanding of the influence of particular lifestyles;instead, the wealth of data has overwhelmed existing systems andmethods. A need exists for methods and systems that allow for systematicanalysis of lifestyle data.

SUMMARY

The invention may include methods and systems involving assembling datafrom at least one data source into at least one life bit, assembling theat least one life bit into at least one life byte and analyzing the atleast one life byte to determine at least one lifeotype. In oneembodiment, each life byte consists of a plurality of life bits, andlife bytes are organized into sequences, each of which can becharacterized as a life byte sequence. In turn, life byte sequences canbe analyzed to identify ones of interest, such as for clinical research,wellness, or the like, such sequences of interest being characterized orexpressed as lifeotypes (as described below).

At least one data source rendering a life bit may be a body monitor,such as one that includes one or more sensors. Examples of body monitorsand other systems, devices, and methods that can be used to generate thedata rendering life bits and ultimately lifeotype data are described indescribed in Stivoric et al., U.S. Pat. No. 7,020,508, issued Mar. 28,2006, entitled Apparatus for Detecting Human Physiological andContextual Information; Teller et al., pending U.S. patent applicationSer. No. 09/595,660, for System for Monitoring Health, Wellness andFitness; Teller, et al., pending U.S. patent application Ser. No.09/923,181, for System for Monitoring Health, Wellness and Fitness;Teller et al., pending U.S. patent application Ser. No. 10/682,759, forApparatus for Detecting, Receiving, Deriving and Displaying HumanPhysiological and Contextual Information; Andre, et al., pending U.S.patent application Ser. No. 10/682,293, for Method and Apparatus forAuto-Journaling of Continuous or Discrete Body States UtilizingPhysiological and/or Contextual Parameters; Stivoric, et al., pendingU.S. patent application Ser. No. 10/940,889, Stivoric, et al., pendingU.S. patent application Ser. No. 10/940,214 for System for Monitoringand Managing Body Weight and Other Physiological Conditions IncludingIterative and Personalized Planning, Intervention and Reporting, andStivoric et al., pending U.S. patent application Ser. No. 11/582,896 forDevices and Systems for Contextual and Physiological-Based Detection,Monitoring, Reporting, Entertainment, and Control of Other Devices, eachof which are incorporated, in their entirety, herein by reference.

The data may include physiological data, contextual data andenvironmental data. The data may also include derived data, analyticalstatus data, contextual data, continuous data, discrete data, timeseries data, event data, raw data, processed data, metadata, third partydata, physiological state data, psychological state data, survey data,medical data, genetic data, environmental data, transactional data,economic data, socioeconomic data, demographic data, psychographic data,sensed data, continuously monitored data, manually entered data,inputted data, continuous data and real-time data.

In an embodiment, at least one of the assembly and analysis of lifotypesmay utilize a wide range of techniques applied to a life byte sequence,a life byte, a life bit, or a lifeotype, in order to yield a prediction,inference, or the like. Such techniques may include, without limitation,iterative optimization, genetic programming, stochastic simulations,model generation, model use, simulated annealing, Markov methods,reinforcement learning, partial programming, stochastic beam search,model based search, goal-based search, goal-based methods, feedbackloops and artificial intelligence. In embodiments, the method may beapplied to medical decision making, disease management, auto-publishing,automatic completion of forms, filtering search results, deliveringcontent, dating, social networking and e-commerce. In embodiments, theat least one lifeotype and any related information may be represented ina spider map or the like or may be superimposed on a map. Inembodiments, the method may further comprise determining the numbers andtypes of life bits and life bytes required to fully determine alifeotype.

The methods and systems disclosed herein may include a method or systeminvolving classifying data concerning a population of individuals intolifeotypes that correspond to certain combinations of aspects of atleast one of the human lifestyle, human status and the human condition,such combinations optionally including combinations of life bytes, lifebyte sequences, life bits, or combinations of other lifeotypes. In anembodiment, the method or system may also involve analyzing patternswithin and across lifeotypes to draw conclusions, draw inferences, ormake predictions about individuals with a certain lifeotype or groups ofindividuals that share a certain lifeotype. At least one data source maybe a body monitor including at least one sensor. The data may includeany of the data sources described herein or in documents incorporated byreference herein, including, for example, physiological data, contextualdata and environmental data. The data may also include derived data,analytical status data, contextual data, continuous data, discrete data,time series data, event data, raw data, processed data, metadata, thirdparty data, physiological state data, psychological state data, surveydata, medical data, genetic data, environmental data, transactionaldata, economic data, socioeconomic data, demographic data, psychographicdata, sensed data, continuously monitored data, manually entered data,inputted data, continuous data and real-time data.

The classification process used to identify a lifeotype may utilize awide range of techniques disclosed herein, in the documents incorporatedby reference herein, or known to those of ordinary skill in the art,including, without limitation iterative optimization, geneticprogramming, stochastic simulations, model generation, model use,simulated annealing, Markov methods, reinforcement learning, partialprogramming, stochastic beam search, model based search, goal-basedsearch, goal-based methods, feedback loops and artificial intelligence.In embodiments, the method or system may be applied to medical decisionmaking, disease management, auto-publishing, automatic completion offorms, filtering search results, delivering content, dating, socialnetworking and e-commerce. In embodiments, the at least one lifeotypeand any related information may be represented in a spider map or thelike or may be superimposed on a map. In one embodiment, the more thanone life byte may be organized into a life byte sequence.

The methods and/or systems disclosed herein may include a systemcontaining a facility for assembling data from at least one data sourceinto at least one life bit, a facility for assembling the at least onelife bit into at least one life byte, and a facility for analyzing theat least one life byte, or a sequence of life bytes, to determine atleast one lifeotype. At least one data source rendering a life bit maybe a body monitor, such as including one or more sensors. The data mayinclude physiological data, contextual data and environmental data. Thedata may also include derived data, analytical status data, contextualdata, continuous data, discrete data, time series data, event data, rawdata, processed data, metadata, third party data, physiological statedata, psychological state data, survey data, medical data, genetic data,environmental data, transactional data, economic data, socioeconomicdata, demographic data, psychographic data, sensed data, continuouslymonitored data, manually entered data, inputted data, continuous dataand real-time data.

In an embodiment, at least one of the facility for assembly and thefacility for analysis of lifotypes may utilize a wide range oftechniques applied to a life byte sequence, a life byte, a life bit, ora lifeotype, in order to yield a prediction, inference, or the like.Such techniques may include, without limitation, iterative optimization,genetic programming, stochastic simulations, model generation, modeluse, simulated annealing, Markov methods, reinforcement learning,partial programming, stochastic beam search, model based search,goal-based search, goal-based methods, feedback loops and artificialintelligence. In embodiments, the system may be applied to medicaldecision making, disease management, auto-publishing, automaticcompletion of forms, filtering search results, delivering content,dating, social networking and e-commerce. In embodiments, the at leastone lifeotype and any related information may be represented in a spidermap or the like or may be superimposed on a map. The system may alsoinclude a facility for determining the numbers and types of life bitsand life bytes required to fully determine a lifeotype.

The methods and systems disclosed herein may include a system with afacility for classifying data concerning a population of individualsinto lifeotypes that correspond to certain combinations of aspects of atleast one of the human lifestyle, human status and the human condition,such combinations optionally including combinations of life bytes, lifebyte sequences, life bits, or combinations of other lifeotypes. In anembodiment, the system may also involve analyzing patterns within andacross lifeotypes to draw conclusions, draw inferences, or makepredictions about individuals with a certain lifeotype or groups ofindividuals that share a certain lifeotype. At least one data source maybe a body monitor including at least one sensor. The data may includeany of the data sources described herein or in documents incorporated byreference herein, including, for example, physiological data, contextualdata and environmental data. The data may also include derived data,analytical status data, contextual data, continuous data, discrete data,time series data, event data, raw data, processed data, metadata, thirdparty data, physiological state data, psychological state data, surveydata, medical data, genetic data, environmental data, transactionaldata, economic data, socioeconomic data, demographic data, psychographicdata, sensed data, continuously monitored data, manually entered data,inputted data, continuous data and real-time data. The data may datarelated to family history, genes, diagnoses, medical knowledge,polygraphs and the like. The data may be collected over time. The datamay be data relevant to a certain measure at various points in time.

The facility for classifying data may utilize a wide range of techniquesdisclosed herein, in the documents incorporated by reference herein, orknown to those of ordinary skill in the art, including, withoutlimitation iterative optimization, genetic programming, stochasticsimulations, model generation, model use, simulated annealing, Markovmethods, reinforcement learning, partial programming, stochastic beamsearch, model based search, goal-based search, goal-based methods,feedback loops and artificial intelligence. In embodiments, the systemmay be applied to medical decision making, disease management,auto-publishing, automatic completion of forms, filtering searchresults, delivering content, dating, social networking and e-commerce.In embodiments, the at least one lifeotype and any related informationmay be represented in a spider map or the like or may be superimposed ona map. In one embodiment, the more than one life byte may be organizedinto a life byte sequence.

In embodiments, the present invention provides a method for assemblingdata from at least one data source into at least one life bit,assembling the at least one life bit into at least one life byte, anddefining a lifeotype based on the attributes of at least one life byte.In embodiments, the data sources may include sensed health data,residence data, medical records, survey data, or some other type of datasources. In embodiments, the life byte may consist of four life bits,with one bit consisting of each of sensed health data, residence data,medical records, survey data, or some other type of data. Further, thelifeotype may relate to a pattern of behavior and sensed values thatindicate that an individual is at very high risk of becoming diabeticlater in life.

In embodiments, the at least one data source may be a body monitorincluding at least one sensor. The data may be a physiological data,contextual data, environmental data, derived data, analytical statusdata, continuous data, discrete data, time series data, event data, rawdata, processed data, metadata, third party data, physiological statedata, survey data, medical data, genetic data, transactional data,economic data, socioeconomic data, demographic data, psychographic data,sensed data, continuously monitored data, manually entered data,inputted data, real-time date, feedback loop data, relative level data,changes in levels data, or some other type of data.

In embodiments, the data may consist of at least two types of data. Inanother embodiment, the data may consist of at least three types ofdata. In embodiments, the data may be a status data. In otherembodiments of the present invention, the data may be a derived data.The derived data may be generated from both a first parameter and asecond parameter of an individual. Both the first and second parametersmay be generated by a sensor device and may be at least one of aphysiological, contextual, environmental, or some other type ofparameter of the individual. In an embodiment, the data may be a databit data. Further, the data bit may consist of at least two types ofdata.

In embodiments, the data may be the derived data generated by aprocessor from a first sensor generating data indicative of at least oneof a first physiological, first contextual and first environmentalparameter of an individual and a second sensor generating dataindicative of at least one of a second physiological, second contextualand second environmental parameter of the individual. Further, thederived data may be a status parameter that cannot be directly measuredwith any of the first and second sensors alone. In embodiments, the datamay be the derived data generated by a processor from a first wearablesensor generating data indicative of at least one of a firstphysiological, first contextual and first environmental parameter of anindividual and a second wearable sensor generating data indicative of atleast one of a second physiological, second contextual and secondenvironmental parameter of the individual. The derived data may be astatus parameter that cannot be directly measured with any of the firstand second sensors alone.

In another embodiment, the data bit data sources may include sensedhealth data, residence data, medical records, survey data, or some othertype of data relevant to the individual. Further, the sensed health datamay consist of yearly blood pressure readings administered at a doctor'soffice and extracted from a personal health record, the residence datamay indicate that the individual lives in an urban area may not beconducive to year-round exercise and may be characterized by longcommute time. The medical record may indicate that the individual isMexican-American and that two of the individual's four grandparents werediabetic before they died, and the survey data may disclose that theindividual exercise very vigorously, but only occasionally with afrequency of about 1.2 times per week for 75 minutes; and the lifeotyperelates to a pattern of behavior and sensed values that indicates thatthe individual may be at very high risk of becoming diabetic later inlife.

In embodiments, the lifeotype may be defined based on analysis ofcorrelation between attributes of a life byte and other informationabout a population of individuals. Further, the lifeotype may be definedbased on analysis of correlation between attributes of a life byte andhealth information about a population of individuals. In anotherembodiment, the lifeotype may be defined based on analysis ofcorrelation between attributes of a life byte and aging informationabout a population of individuals. In yet another embodiment, thelifeotype may be defined based on analysis of correlation betweenattributes of the life byte and fitness information about a populationof individuals. Further, the lifeotype may be defined based on analysisof correlation between attributes of the life byte and performanceinformation about a population of individuals. Still further, thelifeotype may be defined based on analysis of correlation betweenattributes of a life byte and academic performance information about apopulation of individuals. Further, the lifeotype may be defined basedon analysis of correlation between attributes of the life byte and jobperformance information about a population of individuals. Furthermore,the lifeotype may be defined based on analysis of correlation betweenattributes of the life byte and sports performance information about apopulation of individuals.

In an embodiment, the life byte may be comprised of a specified sequenceof life bits, each life bit may be corresponding to a data typeassociated with a specific data source. Further, more than one life bytemay be organized into a life byte sequence. In embodiments, at least oneof the assembly of the life byte and definition of the lifeotype mayutilize at least one of the following techniques such as iterativeoptimization, genetic programming, stochastic simulations, modelgeneration, model use, simulated annealing, Markov methods,reinforcement learning, partial programming, stochastic beam search,model based search, goal-based search, goal-based methods, feedbackloops, artificial intelligence, or some other type of technique.

Further, in an embodiment the lifeotype of an individual may be utilizedas input to at least one of an application and a decision-makingprocess. Furthermore, the lifeotype of an individual may be utilized asinput to at least one of an application and a decision-making process.In embodiments, at least one of the application and the decision-makingprocess may be related to at least one of medical decision making,disease management, auto-publishing, automatic completion of forms,filtering search results, delivering content, dating, social networking,e-commerce, or some other type of application or process. Inembodiments, at least one lifeotype and any related information may berepresented in a spider map. Further, at least one lifeotype and anyrelated information may be superimposed on a map. In embodiments, thenumbers and types of life bits and life bytes may be required to fullydetermine a lifeotype. In embodiments, the present invention provides amethod for classifying a population of individuals into lifeotypes thatcorrespond to defined sequences of status data that are collected withrespect to specified aspects of the human lifestyle.

In another embodiment, the present invention provides a method forclassifying a population of individuals into lifeotypes that correspondto defined sequences of status data. The status data may include dataobtained by at least one sensor associated with the individual. In yetanother embodiment, the present invention provides a method forclassifying a population of individuals into lifeotypes that correspondto defined sequences of status data that are collected with respect tospecified aspects of the human condition. In embodiments, the method mayfurther comprise analyzing patterns within and across lifeotypes to drawconclusions about individuals sharing a certain lifeotype. Inembodiments, the status data may be selected from the group consistingof human status data, human condition data, human lifestyle data, orsome other type of data. In embodiments, the status data may further bea physiological data, contextual data, environmental data, or some othertype of data.

In embodiments, at least a subset of the status data may be obtainedusing a body monitor including at least one sensor. In embodiments, theclassification process may utilize at least one of the techniques suchas iterative optimization, genetic programming, stochastic simulations,model generation, model use, simulated annealing, Markov methods,reinforcement learning, partial programming, stochastic beam search,model based search, goal-based search, goal-based methods, feedbackloops, artificial intelligence, or some other type of technique. Inembodiments, the present invention provides a computer-readable storagemedia in a computer system. The computer-readable storage media maystore a plurality of discrete data bits, the data bits comprising atleast one of information that may be automatically sensed regarding theindividual and information that is input into the system and a pluralityof bytes assembled from sequences of predefined types of the discretedata bits. The bytes may be assembled into a data structure unique tothe individual.

In embodiments, the data bits may include sensed health data, residencedata, medical records, survey data, or some other type of data. Inembodiments, the byte may consist of four bits, with one bit consistingof each of sensed health data, residence data, medical records, surveydata, or some other type of data. In embodiments, the bytes maycorrespond to at least one of a behavior, a condition, a health status,or some other type of attribute. In embodiments, the information may beat least one of the individual's, choices, behavior, activity,physiological status, contextual status, environmental status, or someother type of information. In an embodiment, the unique data structuremay relate to a pattern of behavior and sensed values that may indicatethat an individual is at very high risk of becoming diabetic later inlife. In embodiments, the data structure unique to the individual acrossmore than one individual may be aggregated. In an embodiment, thecomputer program may draw inferences about the population from theaggregation.

These and other systems, methods, objects, features, and advantages ofthe present invention will be apparent to those skilled in the art fromthe following detailed description of the preferred embodiment and thedrawings. All documents mentioned herein are hereby incorporated intheir entirety by reference.

BRIEF DESCRIPTION OF THE FIGURES

The invention and the following detailed description of certainembodiments thereof may be understood by reference to the followingfigures:

FIG. 1 depicts a hierarchy of data.

FIG. 2 depicts a hierarchy of genetics data.

FIG. 3 depicts a hierarchy of lifeotype data.

FIG. 4 depicts certain spectra of certain lifeotype data sources.

FIG. 5 depicts lifeotype data sources.

FIG. 6 depicts the Platform.

FIG. 7 depicts the scalability of the Platform.

FIG. 8 depicts the scalability of lifeotypes.

FIG. 9 depicts the types of data that may comprise life bits data.

FIG. 10 depicts the types of data that may comprise medical and/orgenetic data.

FIG. 11 depicts the types of data that may comprise environmental data.

FIG. 12 depicts the types of data that may comprise derived data.

FIG. 12A depicts various spectra applicable to sensors, data and/or thePlatform.

FIG. 13 depicts the relationship among physiological, contextual andenvironmental data.

FIG. 14 depicts a process flow for identifying lifeotypes.

FIG. 15 depicts a process flow for analyzing lifeotypes.

FIG. 16 depicts a process flow for analyzing lifeotypes.

FIG. 17 depicts a lifeotype state diagram.

FIG. 18 depicts a lifeotype spider map or the like.

FIG. 19 depicts an embodiment of the architecture of the Platform.

FIG. 20 depicts an embodiment of the architecture of the Platform.

FIG. 21 depicts an embodiment of the architecture of the Platform usinground-robin DNS load balancing.

FIG. 22 depicts an embodiment of the architecture of the Platform usingcookie or URL-based sessions with a software load balancer.

FIG. 23 depicts an embodiment of the architecture of the Platform usingcookie-based sessions with a hardware load balancer.

FIG. 24 depicts a particular embodiment of an analogy between alifeotype and genetics.

FIG. 25 depicts a particular embodiment of a statistical modelconcerning lifeotypes.

FIG. 26 depicts a particular embodiments of affecting behavior throughlifeotypes.

FIG. 27 depicts a particular embodiment of lifeotype information beingused for compatibility analysis.

FIG. 28 depicts a particular embodiment of lifeotype information beingused for compatibility analysis.

FIG. 29 depicts a particular embodiment of a report.

DETAILED DESCRIPTION OF THE PRESENTLY PREFERRED EMBODIMENT

Humankind has sequenced the human genetic code, resulting in theidentification of sequences of genes that are related to particularconditions, outcomes or the like. Thus, a certain genotype can beassociated with outcomes, allowing the prediction of outcomes forindividuals or groups that share that genotype. However, despite awealth of information collected about lifestyles, similar efforts havenot been undertaken to sequence data related to the human lifestyle inorder to allow the drawing of the same kinds of inferences aboutindividuals or groups that share the same lifestyle. The low cost andready availability of sensors has reduced costs of collecting data. Inaddition, improved data integration and processing methods have allowedfor use of existing data sources. The availability of this wealth ofdata creates a unique opportunity for data analytics and dataprocessing, which may be used to analyze and parse the wealth of humanlifestyle information. Importantly, methods and systems are disclosedherein for organizing data about lifestyles into meaningful sequences ofinformation, allowing analysis and drawing of inferences about theeffects of human lifestyles. Among other advantages, data processing anddata analytics, applied to life bits, life bytes, life byte sequencesand lifotypes, may also allow for the creation or identification of newsurrogate measures, sensors and vital signs, as well as predictors ofcertain conditions.

Thus, the concept of a “lifeotype” encompasses classifying human statedata, or other data concerning a population or sub-population ofindividuals, into “types” that correspond to certain combinations oftraits or aspects of human lifestyle, human status and/or humancondition. In embodiments, the concept of a lifeotype may also beapplied to other organisms. By analyzing patterns within and across thelifeotypes, one can draw conclusions, make inferences, and makepredictions about each type that apply to the members of the type or togroups of individuals of that type. The possible types may be composedof combinations of individual data types which may be measuredcontinuously over time or at discrete intervals.

Referring to FIG. 1, the concept of a lifeotype may be furtherunderstood by analogy to bits and bytes of information in the dataworld. Information may be organized into bits, bits may be organizedinto bytes, bytes may be organized into sequences, and any of theforegoing may be organized into, or provide, actionable information.Actionable information may be composed of any number, including none, ofbits and/or bytes. The inclusion of bit 0 and byte 0 in FIG. 1illustrates that it is possible that there are no bits and/or bytes in aparticular embodiment. That is, it is possible that the informationitself is a byte or that a bit is actionable information, that theinformation itself is actionable and the like.

By analogy to the bits and bytes of FIG. 1, a life bit may be a bit ofdata for a trait or aspect at a point in time. A life byte may be acollection of life bits. In an embodiment, the bits may be values ofcertain parameters, with bits of certain types (such as derived fromcertain data sources, including the ones described herein) beingarranged in a predetermined way to form a byte. The byte may be anaggregate of the bits, which may for example, correspond to a particulartype of information, such as a type of file, a message, a command, orthe like, in the same way that a particular type of life byte maycorrespond to a particular type of information collected about a humanstate. The bytes may be sequenced or otherwise combined to formactionable information, such that a higher level system, such as anoperating system, application, program, service or the like can take abyte or series of bytes and perform an operation based on the nature ofthe byte or sequence of bytes and in particular the bits that populatethat byte.

Referring to FIG. 2, the concept of a lifeotype may be furtherunderstood by analogy to genetics. Genetic information may be organizedin base pairs or genetic sequences and in their totality comprise thegenotype. Life bits can be thought of as analogous to genes, which areorganized according to the sequence of the genotype, but may or may notbe expressed in a given individual, or may be expressed to a differentextent in a particular individual. Particular genes or sequences ofgenes that are expressed (including, in some cases, expressed to aparticular extent) and that, taken together, are of interest, may beassembled into genotypes, in the same way that life bytes or sequencesof life bytes that are of interest may be assembled into lifeotypes. Thegenotypes in turn, through the interaction with the environment in somecases, may present as an overall phenotype, analogous to actionableinformation. As with FIG. 1, the inclusion of the subscript zeros inFIG. 2 indicates that a particular level of the hierarchy may be absentin certain embodiments.

FIG. 3 depicts the organization structure of FIGS. 1 and 2 applied tolifeotypes. Referring to FIG. 3, the information or genetic sequencesmay be data, such as any of the data described herein, from any of thesources described herein. The data may be combined, used or accessed tocreate life bits. The life bits may be combined, used or accessed tocreate life bytes. A grouping or sequence of lifebytes may form alifebyte sequence. Lifebytes and/or one or more lifebyte sequences maycomprise or be organized into lifeotypes. The amount of information,number of life bits and/or number of life bytes included in a lifeotypemay be determined based on many factors, such as user selection or thenumber of data points required to obtain uniqueness. As in FIGS. 1 and2, the inclusion of the subscript zeros indicates that a particularlevel of the hierarchy may be absent in certain embodiments.

The entire range of data collected about an individual may be analogousto the entire genotype of an individual, and particular combinations inthe data patterns may be analogous to genes or collections of genes thatcode for particular traits. As with a particular genotype, a particularlifeotype may code for or represent a particular set of traits. Alifeotype may change over time, including reasons such random changereasons due to therapy, such as behavior modification therapy, reasonsdue to other changes in an individual's behavior, how the individualinteracts with his environment and vice-verse, and due to modifications,or additions to the amount and type of information being collected aboutan individual. This process may be analogous to gene mutations and genetherapy in genetics. Regarding therapeutics, the therapeutics processmay be intentional or non-intentional and/or prescribed orself-administered. The pool of data may be less than the total pool ofdata, which may be analogous to sequencing less than all of the geneticcode of an individual in genetics. Referring again to FIGS. 1, 2 and 3,it may be possible to move in both directions in the hierarchiesdepicted in the figures. For example, in FIG. 3, the data or life bitsmay be determined from a life byte or lifeotype. In FIG. 1, it may bepossible to work from actionable information back to information.

In an embodiment, a life bit may be body positional data, such assitting or standing. A related life byte may be standing more thansitting. This life byte may contribute to the determination of alifeotype which may be characterized as one relating to the condition ofvaricose veins. In another embodiment, the data may include financialand transaction data. The related life bits may include certaintransactions and financial data. These life bits may be aggregated intoa financial status life byte.

In another embodiment, a particular lifeotype may be that of adepressive. The data on which this lifeotype is based may include surveydata, financial data, transaction data, medical data and sensor data.Sensors, such as the type described in United States patent applicationsincorporated herein by reference provide sensed data from which aderivation could be made regarding an individual's activity level, foodintake, mood, and interaction with others. All of such sensed data ineach patent application incorporated herein by reference is relevant tothis and all other embodiments described herein. A relevant life bit maybe composed of credit card purchases, and a relevant life byte mayreveal that the majority of purchases were online and few were at pointof sale terminals, thus revealing that the individual tends to stay inone location. The survey data may result in a life byte that indicatesthe individual is depressed. The sensor data may show that theindividual spends most of his time in one location due to low levels ofactivity, and that the individual has limited interaction with others.These factors together may be a lifeotype or marker for a depression,analogous to a genetic marker or the genotype of an individual that isdepressed.

In another embodiment, a lifeotype may be a hypertensive, diabeticrunner. The data on which this lifeotype is based may include surveydata, medical data and sensor data. Certain of the relevant life bytesmay include age related information, bone density related informationand a diabetic life byte. The values of these life bytes may indicate ahigh likelihood of hypertension and low bone density. The Platform maysuggest additional data that should be collected for furtherinvestigation. A sensor may provide many activity life bits, which mayindicate an overall active life byte. The Platform may sequence the lifebytes to find the lifeotype to be a runner with low bone density,hypertension and diabetes.

In another embodiment, a particular lifeotype may be that of an activediabetic. This lifeotype may be a 4 byte lifeotype, where life byte 1 isa glucose reading, life byte 2 is a pancreas function measurement ofsome kind, life byte 3 is total calories consumed in a day and life byte4 is total calories burned in exercise. Each byte may be composed ofseveral life bits. In an embodiment, total calories burned may bedetermined from life bits including activity level data as determined bysensor data and food intake data as determined from a survey or any ofthe systems, devices or methods described in the patent applicationwhich are incorporated herein by reference. Certain of the life bytesmay originate directly from the data, such as glucose readingsdetermined directly from a glucose meter. The resulting life bits andlife bytes may be packaged into their own data structures, such as apacket header

In an embodiment, a lifeotype may be a pattern of behavior and sensedvalues that indicates that an individual is at a very high risk ofbecoming diabetic later in life. In an embodiment, the lifeotype may bedefined by four lifebytes. The first life byte may be composed of sensedhealth data life bits such as yearly blood pressure readingsadministered at a doctor's office and extracted from the individual'selectronic medical record or personal health record. The second lifebyte may be residence data revealing that the individual lives in anurban area that is not conducive to year-round exercise and that ischaracterized by very long commute times. The third life byte mayconsist of data from a medical record and may indicate that theindividual is Mexican-American and that two of the individual's fourgrandparents were diabetic before they died. The fourth life byte mayconsist of survey data and may indicate that the individual exercisesvery vigorously, but only occasionally with a frequency of 1.2 times perweek and only for average of 75 minutes each time. In an alternativeembodiment, a life byte may be that an individual is at a very high riskof becoming diabetic later in life and the life bits may be sensedhealth data, residence data, medical record data and survey data. Inanother embodiment, the lifeotype may be related to diabetes,hemorrhagic shock or hypertension. The data bits may related to geneticmarkers, diagnoses, plans for therapy, sensed data regarding physicalactivity, such as from a wearable device, energy expenditure,nutritional data and the like.

A genotype may be conceived of as an encoding of what may happen to aperson through the process of developmental biology, similar to ablueprint for a house. This genetic blueprint may also be thought of asthe gold-standard for the house, the platonic house, or the defaulthouse, based on all of which variation will occur. The genotype may alsoset the basic rules for how that physical body will function in responseto particular kinds of changes to that body. By analogy, this may belike the house having a built in furnace and thermostat and being set toturn on the heat when the thermostat drops below a particulartemperature.

A genotype may have various levels of abstractions that are useful tounderstand about the way that encoding (that blueprint) is translatedinto a physical system or the basic rules of operation of that physicalsystem. A genotype in a human is made up of atoms, but that is often toofine grained a level of detail and is not usually considered a usefulway to talk about the genotype. The lowest level of abstraction normallyused for a genotype are the base pairs that make it up (“A T C and G”).

The state of your body at some point in the future may not entirely bedetermined by its genetic make up. Genetics may have, over time, only aminority impact on the state of a person's body. The other relevantelements may be the things that happen to a body. A simple illustrativeexample is as follows: if a car side-swipes a person and breaks theperson's leg, the body has changed dramatically and not because ofgenetics (although genetics may affect the extent of the break, the timeto heal and the like). Similarly, if a person eats too much over a longperiod of time and becomes obese, this was not a fact solely related tothe person's blue print (genotype) but of the complex web of cause andeffect interactions that the person has with the world as the personlives his life (although genetics may affect that person's interactionwith the world, such as by determining at least in part the effects thatfood has on the person's body). In one particular embodiment, this datacollected about a person that corresponds to the series of things thathappens to a person or because of a person's choices which determines toa large extent what will happen to a person in the future can be thoughtof as a lifeotype. In certain embodiments, a lifeotype may also includeor be based on genetics-related information (as bits, bytes, life bytesequences, etc.), as well as any of the other information discussedherein.

Referring to FIG. 24, in one particular embodiment, like the humangenotype, the human lifeotype may have various levels of abstraction. Inthis particular embodiment, at the lowest level (the equivalent to thebase pairs), are all the facts of what happened to a person expressed intheir raw “sensed” values. An example is as follows: each key strokethat a person made at his/her computer, each acceleration a person'sbody experiences as it moves about daily life, a person's heart rate ateach minute of the day, and the like. In this particular embodiment, theequivalent to the alleles and their relative importance (intron vs.extron) may be the notion of a continuum from “derived data” through“patterns of data.” So for example, thinking about many of the sensedvalues about a person's body not in isolation but taken together in amodel of energy expended may be a “derived” lifeotype fact in thisparticular embodiment. In this particular embodiment, at a higher levelof derivation or pattern finding might be that over a period of timeenergy expenditure is high enough to qualify as an “active person.” And,in this particular embodiment, up at the level, by analogy, of achromosome for a lifeotype may be the notion of the implication of majorpatterns of the data of your life upon the future state of your body.For example, being an active person makes obesity, diabetes, depression,and heart disease all some what less likely to occur to you. In thisparticular embodiment, just like gene therapy is an attempt to improve aperson's body in the future by changing some of the genetic blue print,an individual could also receive a suggested change to his lifeotypethat would tend to improve his body's future state as well. For example,“a person may not be an active person and if he was to exercise anadditional 60 minutes per week, raising him into the state of being anactive person, he is less likely to develop the following diseaseswithin the next two decades . . . .” This type of suggested action maybe an action type, or A-type. FIG. 25 depicts a particular embodiment ofa statistical model involving lifeotypes. In this embodiment,conditional probabilities may be determined based on lifeotypes. Oneskilled in the art will appreciate that the analogies described hereinare for illustrative purposes and should not serve to limit the meaningof terms described herein. None of the usages of the terms in theanalogies or examples herein are intended to contradict the meaning ofany term in this disclosure, but rather as alternate meanings or nuancedmeanings of the terms.

Referring to FIG. 4, the data may include continuous or discrete data orany form of data that may be found along this spectrum. In anembodiment, the data may be continuous temperature data and/or adiscrete measure such as a voltage. The data may include raw or deriveddata or any form of data that may be found along this spectrum. The rawdata may be unprocessed. The derived data may be derived from the rawdata, other derived data or a combination of both. The data may besensed by a body monitor and/or a sensor, which may be stationary,wearable or implantable, or any form that may be found along thisspectrum. A stationary sensor may be housed in an item of fitnessequipment, such as a treadmill. A wearable sensor may be included aspart of an arm band, shirt or shoe. In an embodiment, an implantablesensor may be a heart rate sensor implanted near the heart. Referring toFIG. 5, a lifeotype may or may not be constructed from at least one itemof discrete or continuous data, raw or derived data and/or data sensedby a body monitor and/or sensor which may be stationary, wearable orimplantable. The inclusion of the subscript zeros in FIG. 5 indicatesthat a particular level of the hierarchy may be absent in certainembodiments.

A lifeotype may be static or dynamic or may exist in a form found alongthis spectrum. That is, a lifeotype may consist of data that is morestatic over time or data that is more dynamic over time. A lifeotype maybe high resolution or low resolution or may exist in a form along thisspectrum. That is, a lifeotype may consist of a variety of life bytes,life bits and data, which would make it a lifeotype of a higherresolution when compared to a lifeotype that is based on relatively fewlife bytes, life bits and data instances. A static lifeotype and a highresolution lifeotype may respond in similar ways to changes in the dataon which each is based. This behavior similarity may be due to a greaternumber and variety of life bytes, life bits and data instances beinginvolved, so it requires a greater change in the underlying factors anddata to produce a change at the lifeotype level. A dynamic lifeotype anda low resolution lifeotype may respond in similar ways to changes in thedata on which each is based. This behavior similarity may be due to alower number and low variety of life bytes, life bits and data instancesbeing involved, so it requires only a change in one or a few values ofthe underlying factors and data to produce a change at the lifeotypelevel. In embodiments, a low resolution and/or dynamic lifeotypes, orthe life byte sequences, life bytes, life bits and/or data upon whichthey are based, may include angry, aroused, tired, fatigued, currentspending, location, restless, stressed and the like. In embodiments, ahigh resolution and/or static lifeotypes, or the life byte sequences,life bytes, life bits and/or data upon which they are based, may includedepressed, addict, diabetic (type I and II), insomniac, cardiaccondition and the like. In certain embodiments a high resolutionlifeotype may change rapidly over time and a low resolution lifeotypemay change more slowly over time. Lifeotypes can be true orrepresentative at specific points or ranges of time in a person's life.Lifeotypes may reflect different time scales.

The Platform may be able to determine and/or display the direction of alifeotype. In this way, the direction of trend of a lifeotype and/orgroup of lifeotypes can be determined. This information may be usefulfor identifying and/or predicting changes in high resolution and/orstatic lifeotypes. In an embodiment, due to the possibly variable natureof a low resolution and/or dynamic lifeotypes, such lifeotypes may beconceived of or reported with a tolerance band based on related trendinformation and predictions. In another embodiment, the trendinformation and predictions may be useful in predicting emergencies inconnection with low resolution and/or dynamic lifeotypes and diseasestates in connection with high resolution and/or static lifeotypes.Lifeotype trend information, including trend directions, may be usefulfor treating certain conditions for which certain parameters need to bekept in a certain range. In an embodiment, certain lifeotypes of bipolarindividuals may need to be kept within a certain range for a certainparameter, such as mood or endorphin levels. Using the trend directionfunctionality it may be possible to affect the trend as the lifeotypevalue approaches the boundary of the range.

A system for creating, analyzing and making use of lifeotypes maycontain various layers, facilities and/or functionalities (the“Platform”).

FIG. 6 depicts one particular embodiment of the Platform. The variouslayers, facilities and/or functionalities may appear in an order orarrangement different from that shown in FIG. 6. Referring to FIG. 6,the Platform may contain data and/or data sources, a data interface,data processing, life bits, life bit processing, life bytes, life byteprocessing, life byte sequences, lifeotype data processing, interfaces,lifeotypes, lifeotype systems, applications and/or services, users, datatargets, other systems applications and/or services and dataadministration, including security, logging, conditional access and/orauthentication.

The data and/or data sources may be any of the data described herein ormay be from any of the sources described herein. The data and/or datasources may include data from sensors, user input and/or other sourcesas described herein. The data and/or data sources may includephysiological data, contextual data and/or environmental data asdescribed herein.

The data interfaces layer may contain adaptors and/or connectors whichallow the Platform to communicate with various disparate data sources.In an embodiment, a connector may permit the Platform to obtain patientdata from a particular hospital database, such as a patient admissiondatabase. The data interfaces layer may be or contain an interface tosources and targets. The data interfaces layer may be based on a pushmodel, pull model or both. The data interfaces layer may includesearch/filter/cluster functionality.

The data processing layer may enable analytics and derivation. The dataprocessing layer may create, generate, identify and/or discoverlifebits. The data processing layer may search for patterns in the datato create lifebits. The data processing layer may mine data. The dataprocessing layer may identify missing information, which may assist inthe creation, generation, identification and/or discovery of life bits.In an embodiment, the data processing layer may identify a life bit theknowledge of which may be germane to a particular purpose and may alsoidentify the data that is required to be collected in order to determinethat life bit. The data processing layer may analyze life bits andrelated data. The data processing layer may generate conclusions,predictions and/or recommendations. The data processing layer mayidentify patterns in the life bits. The data processing layer maysequence the life bits.

The data processing layer may generate reports. The data processinglayer may auto-publish information, such as reports and studies. Thedata processing layer may auto-complete forms, such as medical recordsand insurance forms. The data processing layer may process, organize andmanage life bits. The data processing layer may clean and de-duplicatelife bits data. The data processing layer may perform extractions,transformations and loads of the life bits data. The data processinglayer may convert life bits data to a common format. The data processinglayer may aggregate, combine and collect life bits data. The dataprocessing layer may request missing data. The data processing layer maycreate databases and datamarts of life bits data and/or other data. Thedata processing layer may associate metadata with the life bits data.

The data processing layer may filter and/or apply contextual structuresto life bits data. The data processing layer may apply algorithms tolife bits data. The data processing layer may enable annotation of, ormay auto-annotate, life bits data. The data processing layer may bebased on a push model, pull model or both. The data processing layer mayprocess and/or clean data. The data processing layer may allow data frommultiple sources to be combined. The data processing layer may organizeand manage data. The data processing layer may enable storage and/orretrieval of data. The data processing layer may enable storage andretrieval of information based on or derived from the data. The dataprocessing layer may store and/or retrieve metadata. The data processinglayer may read and/or write data and metadata. The data processing layermay enable versioning and/or partitioning. The data processing layer maypredict future life bits. The data processing layer may compare a set oflife bits to the genotype and determine the degree of presence of otherlife bits.

Life bit(s), as described herein, may be determined directly from thedata, from a data interface and/or through data processing. A life bitprocessing layer may enable analytics and derivation. The life bitprocessing layer may create, generate, identify and/or discover lifebytes. The life bit processing layer may search for and identifypatterns in the data to create life bytes. The life bit processing layermay mine data. The life bit processing layer may identify missinginformation, which may assist in the creation, generation,identification and/or discovery of life bytes. In an embodiment, thelife bit processing layer may identify a life byte the knowledge ofwhich may be germane to a particular purpose and may also identify thedata that are required to be collected for that life byte. The life bitprocessing layer may analyze life bits and related data. The life bitprocessing layer may generate conclusions and/or recommendations. Thelife bit processing layer may identify patterns in the life bits andlife bytes. The life bit processing layer may identify missinginformation.

The life bit processing layer may generate reports. The life bitprocessing layer may auto-publish information, such as reports andstudies. The life bit processing layer may auto-complete forms, such asmedical records and insurance forms. The life bit processing layer mayprocess, organize and manage life bits. The life bit processing layermay clean and de-duplicate life bits data. The life bit processing layermay perform extractions, transformations and loads of the life bits andlife bytes data. The life bit processing layer may convert life bits andlife bytes data to a common format. The life bit processing layer mayaggregate, combine and collect life bits and life bytes data. The lifebit processing layer may request missing data. The life bit processinglayer may create databases and datamarts of life bits, life bytes and/orother data. The life bit processing layer may associate metadata withthe life bits and life bytes.

The life bit processing layer may filter and/or apply contextualstructures to life bits and life bytes data. The life bit processinglayer may apply algorithms to life bits and life bytes data. The lifebit processing layer may enable annotation of, or may auto-annotate,life bits and life bytes data. The life bit processing layer may bebased on a push model, pull model or both. The life bit processing layermay process and/or clean data. The life bit processing layer may allowdata from multiple sources to be combined. The life bit processing layermay organize and manage data, such as life bits and life bytes data. Thelife bit processing layer may aggregate and/or collect data, such aslife bits and life bytes data. The life bit processing layer may enablestorage and/or retrieval of data, such as life bits and life bytes data.The life bit processing layer may enable storage and/or retrieval ofinformation based on or derived from data, such as life bits and lifebytes data. The life bit processing layer may store and/or retrievemetadata. The life bit processing layer may read and/or write data andmetadata. The life bit processing layer may enable versioning and/orpartitioning.

Life byte(s), as described herein, may be determined directly from thedata, from a data interface and/or through data processing. A life byte,as described herein, may be a life bit and/or may be determined throughlife bit processing. A life byte processing layer may sequence lifebytes. The life byte processing layer may determine lifeotypes. The lifebyte processing layer may enable analytics and derivation. The life byteprocessing layer may create, generate, identify and/or discover lifebytes and/or life byte sequences. The life byte processing layer maysearch for and identify patterns in the data to create life bytes and/orlife byte sequences. The life byte processing layer may mine data. Thelife byte processing layer may identify missing information, which mayassist in the creation, generation, identification and/or discovery oflife bytes and/or life byte sequences. In an embodiment, the life byteprocessing layer may identify a life byte and/or life byte sequence theknowledge of which may be germane to a particular purpose and may alsoidentify the data that are required to be collected for that life byteand/or life byte sequence. The life byte processing layer may analyzelife bytes and/or life byte sequences and related data. The life byteprocessing layer may generate conclusions and/or recommendations. Thelife byte processing layer may identify patterns in the life bytesand/or life byte sequences. The life byte processing layer may generatea genotype of life byte sequences. The life byte processing layer mayidentify missing information.

The life byte processing layer may generate reports. The life byteprocessing layer may auto-publish information, such as reports andstudies. The life byte processing layer may auto-complete forms, such asmedical records and insurance forms. The life byte processing layer mayprocess, organize and manage life bytes and/or life byte sequences data.The life byte processing layer may clean and de-duplicate life bytesand/or life byte sequences data. The life byte processing layer mayperform extractions, transformations and loads of the life bytes and/orlife byte sequences data. The life byte processing layer may convertlife bytes and/or life byte sequences data to a common format. The lifebyte processing layer may aggregate, combine and collect life bytesand/or life byte sequences data. The life byte processing layer mayrequest missing data. The life bit processing layer may create databasesand datamarts of life bytes, life byte sequences data and/or other data.The life byte processing layer may associate metadata with the lifebytes and/or life byte sequences data.

The life byte processing layer may filter and/or apply contextualstructures to life bytes and/or life byte sequences data. The life byteprocessing layer may apply algorithms to life bytes and/or life bytesequences data. The life byte processing layer may enable annotation of,or may auto-annotate, life bytes and/or life byte sequences data. Thelife byte processing layer may be based on a push model, pull model orboth. The life byte processing layer may process and/or clean data. Thelife byte processing layer may allow data from multiple sources to becombined. The life byte processing layer may organize and manage data,such as life bytes and/or life byte sequences data. The life byteprocessing layer may aggregate and/or collect data, such as life bytesand/or life byte sequences data. The life byte processing layer mayenable storage and/or retrieval of data, such as life bytes and/or lifebyte sequences data. The life byte processing layer may enable storageand/or retrieval of information based on or derived from data, such aslife bytes and/or life byte sequences data. The life byte processinglayer may store and/or retrieve metadata. The life byte processing layermay read and/or write data and metadata. The life byte processing layermay enable versioning and/or partitioning.

A life byte sequence, as described herein, may be determined directlyfrom the data, from a data interface and/or through data processing, maybe a life bit and/or may be determined through life bit processing, maybe a life byte and/or may be determined though life byte processing. Alifeotype data processing layer may identify lifeotypes. The lifeotypedata processing layer may enable analytics and derivation. The lifeotypedata processing layer may create, generate, identify and/or discoverlifeotypes. The lifeotype data processing layer may search for andidentify patterns in the data to create lifeotypes. The lifeotype dataprocessing layer may mine data. The lifeotype data processing layer mayidentify missing information, which may assist in the creation,generation, identification and/or discovery of lifeotypes. In anembodiment, the lifeotype data processing layer may identify a lifeotypethe knowledge of which may be germane to a particular purpose and mayalso identify the data that are required to be collected for thatlifeotype. The lifeotype data processing layer may analyze life bytesequences, lifeotypes and related data. The lifeotype data processinglayer may generate conclusions and/or recommendations. The lifeotypedata processing layer may identify patterns in the life byte sequencesand/or lifeotypes. The lifeotype data processing layer may generate a“genome” of lifeotypes. The lifeotype data processing layer may identifymissing information.

The lifeotype data processing layer may generate reports. The lifeotypedata processing layer may auto-publish information, such as reports andstudies. The lifeotype processing layer may assemble lifeotypes into a“genome”. The lifeotype data processing layer may auto-complete forms,such as medical records and insurance forms. The lifeotype dataprocessing layer may process, organize and manage life byte sequencesand/or lifeotypes data. The lifeotype data processing layer may cleanand de-duplicate life byte sequences and/or lifeotypes data. Thelifeotype data processing layer may perform extractions, transformationsand loads of the life byte sequences and/or lifeotypes data. Thelifeotype data processing layer may convert life byte sequences and/orlifeotypes data to a common format. The lifeotype data processing layermay aggregate, combine and collect life byte sequences and/or lifeotypesdata. The lifeotype data processing layer may request missing data. Thelifeotype data processing layer may create databases and datamarts oflife byte sequences, lifeotypes data and/or other data. The lifeotypedata processing layer may associate metadata with the life bytesequences and/or lifeotypes data.

The lifeotype data processing layer may filter and/or apply contextualstructures to life byte sequences and/or lifeotypes data. The lifeotypedata processing layer may apply algorithms to life byte sequences and/orlifeotypes data. The lifeotype data processing layer may enableannotation of, or may auto-annotate, life byte sequences and/orlifeotypes data. The lifeotype data processing layer may be based on apush model, pull model or both. The lifeotype data processing layer mayprocess and/or clean data. The lifeotype data processing layer may allowdata from multiple sources to be combined. The lifeotype data processinglayer may convert data to a common format. The lifeotype data processinglayer may organize and manage data, such as life byte sequences and/orlifeotypes data. The lifeotype data processing layer may aggregateand/or collect data, such as life byte sequences and/or lifeotypes data.The lifeotype data processing layer may enable storage and/or retrievalof data, such as life byte sequences and/or lifeotypes data. Thelifeotype data processing layer may enable storage and/or retrieval ofinformation based on or derived from data, such as life byte sequencesand/or lifeotypes data. The lifeotype data processing layer may storeand/or retrieve metadata. The lifeotype data processing layer may readand/or write data and metadata. The lifeotype data processing layer mayenable versioning and/or partitioning.

A lifeotype, as described herein, may be determined directly from thedata, from a data interface and/or through data processing, may be alife bit and/or may be determined through life bit processing, may be alife byte and/or may be determined though life byte processing, may be alife byte sequence and/or may be determined through lifeotype dataprocessing. The Platform may contain an interface which may be aninterface layer or interface facility. The interface may contain a userinterface and/or presentation facility. The interface may publishreports, studies, conclusions and/or reports. The interface mayautomatically complete reporting documents and forms, such as medicalrecords and insurance forms. The interface may auto-publish information,such as reports and studies. The interface may contain adaptors and/orconnectors which allow the Platform to communicate and/or interface withother systems, facilities, data sources and the like. The interface mayinterface with an outside workflow, which may allow the platform toaffect, optimize or improve efficiency of the outside workflow.

The interface may generate different views of the lifeotype data and/orother data. The interface may filter the lifeotype data and/or otherdata. The filtering may be done by sorting on a particular life bit,life byte and/or lifeotype, such as a medical condition or a state ofactivity. The filtering may also be done by sorting for a particularcombination or combinations of life bits, life bytes or lifeotypes, suchas sorting for all diabetics who are between the ages of 25 and 30 yearsold, engage in at least 10 hours of physical activity per week and eatmore than 3 servings of vegetables per day. Filtering may allow for theidentification of subsets of the data, which may be used for furtherstudies. The interface may include an interface to sources and targets.The interface may function as a data clearinghouse.

The interface may include and/or be enabled or facilitated by alifeotype markup language (“LML”). The interface may use or permitcommunication through LML. LML may facilitate the identification,creation, processing, manipulation and use of lifeotypes. LML may be aprotocol. LML may be embodied in a header. LML may allow interfaces withother systems, platforms and the like, or may allow interfaces betweenelements of the Platform. LML may contain tags, which may function asconnectors or links. The tags may link to other relevant data, or todata sources or sources of data values used in a particular calculation,derivation or analysis. A tag may link to other data, measured values orinformation that may be relevant or related, such as informationrecorded or created around the same time as the other data. A tag maylink to information about mood or food consumption. In an embodiment,the LML corresponding to an energy expenditure calculation may containlinks to data concerning the mood of the subject, food consumed by thesubject and/or other medical values recorded at the time. A tag mayenable a user to quickly locate or query data that form the basis ofother information, derived measures and/or lifeotypes.

In one embodiment, LML may allow the specification of statements thatinclude information about who the statement is about (at multiple levelsof detail); what facts, if any, the statement is about; what patterns,if any, the statement is about; what actions or action sequences thestatement is about; what time points or time periods the statement isabout; what time points or time periods apply to the facts; any groups,patterns, or actions/action sequences; and the like. Abstraction todifferent levels of detail may be allowed for various features of LML.Abstraction to different levels of detail may be optional for eachstatement and certain fields may be optional in respect of a certainstatement. In an embodiment, LML may utilize XML and may include theability to have functional links and the like which may performoperations on a lifeotypes database.

A user interface may be tailored based on the user's lifeotype. A userinterface may contain sliders, pistons or other means to adjustparameters. The user interface may show the effects of changes ofcertain parameters, such as on other parameters, or on lifeotype,medical conditions and the like. The user interface may show the effectsof perturbing the system. Through the user interface it may be possibleto tweak one or more sliders or adjust parameters in other ways and seethe effect or predicted effect of those adjustments on other valuesand/or lifeotypes. Parameters that can be adjusted include theparameters in Table 3 of Andre, et al., pending U.S. patent applicationSer. No. 10/682,293, for Method and Apparatus for Auto-Journaling ofContinuous or Discrete Body States Utilizing Physiological and/orContextual Parameters. The parameters disclosed therein apply to allembodiments herein utilizing sensed or measured data. The user interfacemay present reports, which may be auto-published, may include acomparison to other members of population and/or a comparison to othermembers of same or similar lifeotype profiles. A report may containpredictions, such as the probability of breaking a bone, having astroke, having a major depressive episode and the like and may includerecommendations on behavior, medication and the like. The report mayinclude an interface with sliders that allow a user to perturb therecommendations and/or other aspects of the report and see the effects.

The Platform may contain users, which may be any of the users, consumersor parties described herein. The Platform may include data targets,which may be any of the databases or data structures described herein,including third party data sources. The Platform may contain a lifeotypesystems, applications and/or services layer or facility which may enableany of the systems, methods, apparatuses, applications and/or servicesdescribed herein. The Platform may also contain other systems,applications and/or services, which may be any of the systems, methods,applications and/or services described herein. The Platform may includea data administration layer, which may prohibit, restrict, enable and/orallow access to the Platform or particular aspects of the Platform basedon certain factors. The data administration layer may enable conditionalaccess. In an embodiment, access may be restricted by time, log-inlocation, whether the user is a participant in current study and thelike. The data administration layer may enable differential levels ofaccess. In embodiments, certain users may have access to only certaininformation, functions, data, results and the like. The dataadministration layer may enable logging, identification, authentication,security and privacy protection. The data administration layer maycontain an anonymizer or one or more systems and/or methods by whichusers can opt-in and/or opt-out of certain aspects of the Platform oruses of information related to them. The opt-in/opt-out decision may belinked to a royalty system as discussed herein.

Referring to FIG. 7, the Platform may be scalable. In this regard,several different Platforms could be linked together or linked Platformscould be separated. Various different lifeotypes or lifeotypes ofdifferent people could be linked together or separated. Referring toFIG. 8, two or more lifeotypes can be linked or aggregated together tocreate new lifeotypes. In addition, a lifeotype may be separated intotwo or more lifeotypes.

The data discussed herein may be any measurable, describable orquantifiable aspect of the human condition and/or environment. The datamay be human state data. The data may be energy expenditure data-energyexpenditure data, which may act as a surrogate for vital sign data.Referring to FIG. 9, the data may fall into one or more generalcategories of data, including derived data, analytical status data,contextual data, continuous data, discrete data, time series data, eventdata, raw data, processed data, metadata, third party data, dataregarding physiological state, data regarding psychological state,survey data, medical data, genetic data, environmental data,transactional data, economic data, socioeconomic data, demographic data,psychographic data, sensed data, continuously monitored data, manuallyentered data, inputted data, relative levels, changes in levels andfeedback loop data. In embodiments, the data may be constructed ofderived data and a basic parameter to determine an inverse. Inembodiments, the data may be constructed of derived data andenvironmental data. In embodiments, the data may be constructed ofderived data and physiological data. The physiological data may includeinformation regarding a disease condition and the progress of thedisease (becoming better or worse).

The data may also be specific instances of data, such as any variable orfield of the Platform. A specific instance of data may be data regardingphysiological and/or psychological state. Referring to FIG. 10, the datamay be medical data. The medical data may be diabetes related data (suchas glucose level), family histories, patient records, medication,medical conditions, morbidities, psychological data (such as personalitytype), weight data, height data, cardiac status data, hormone level data(such as for cortisol, insulin, thyroid hormones, HGH, paracrine systemhormones and/or endocrine system hormones), data relating to medicalconditions (such as type I diabetes, type II diabetes or a particularsyndrome), data relating to markers, data relating to seizures, datarelating to fainting, metabolic rate data, data relating to physicalmeasurements and/or conditions (such as a weakened heart wall), geneticdata (such as data concerning genetic conditions, genetic markers,particular genetic sequences and presence or absence of one or moregenotypes and/or phenotypes) and/or data relating to diagnostics.

The data may be transactional data, such as data concerning goods orservices purchased, consumed and/or desired. The transactional data maybe from credit or debit card purchases, from third party databases, frommanually entered data (such as user entered data), from a purchasingprogram associated with the Platform, from internet browsing history,from items placed on layaway, from needs anticipated or predicted by thePlatform, from a record of online purchases and the like. Thetransactional data may relate to grocery purchases, usage of differentutilities (such as water, hydro, gas and the like) and the like. Thetransactional data may include predictions based on past data. The datamay be measured with sensor-packages monitoring multiple individuals.

Referring to FIG. 11, the data may include environmental data.Environmental data may include data relating to light level (such as forsunlight and/or artificial light), weather, ambient temperature,humidity, wind, air quality, atmospheric conditions, water quality,environmental problems, location and/or nutrition (such as concerningfood, beverages, vitamins and/or diet). The data may include contextualand/or situational data. The contextual and/or situational data mayrelate to social context. In an embodiment, the social context may beout with friends or at home alone. The contextual and/or situationaldata may relate to life-cycle context. In an embodiment, the life-cyclecontext may be in college, in the workforce, married with children andthe like. The contextual and/or situational data may relate to activitylevel (such as sedentary or exercising), meditation state, bodyposition, travel (such as in a car, on a plane, on a train, at sea andthe like), shopping, entertainment level (such as at a concert, movieand the like), location (such as determined by GPS or triangulation),miles driven as a passenger, miles driven as a driver, where driven,travel destinations, type of work (such as physical labor or deskwork),hours worked, sleeping, resting and/or arguing.

The data may include personality and/or psychological data. Thepersonality and/or psychological data may include data relating toentertainment choices, mood, amount of time spent reading, books read,topics of material read, authors of material read, amount of fictionread, amount of non-fiction read, amount of time spent watchingtelevision and movies, television programs watched, movies watched,topics of television programs watched, topics of movies watched, moodsof television programs watched, moods of movies watched, amount of timespend playing games and videogames, games or videogames played, topicsof games or videogames played, moods of games or videogames played,skill level of games or videogames played, levels obtained in games orvideogames played, activity level determined from games or videogamesplayed (such as for a Nintendo Wii console), amount of time spent oncertain websites, language context typed into keyboard, voice stresslevels, entertainment choices, leisure choices, choice of sports, choiceof active lifestyle versus sedentary lifestyle, estimated mental statedata (such as data concerning intentions) and the like.

Referring to FIG. 12, the data may be derived data. The derived data mayrelate to stress, cortisol level, activity level, energy expenditure,heart rate variability, hydration, pulse oximetry, profusion of smallvessels, sleep state, sleep onset, VO2 from energy expenditure, glucosefrom energy expenditure, pain from energy expenditure, combinations ofderived parameters and the like.

The data may also include metadata. The metadata may include dataregarding when a particular item of data was measured, how the item wasmeasured, where a particular item of data was measured, the context inwhich the item of data was measured, who measured the item, otherrelated items of data that were measured, the reason the item of datawas measured, relationships of the item to other items, related itemsthat were not measured or recorded, other items with which the data itemis shared and the like. The metadata may include information regardinghow the item of data came to be and how the item of data acts in itsnatural state. Related items of data may be measured at different timesand places, by different methods and for different purposes. The datamay include action state information, activity state information,project state information and relationship information, including databetween and/or among individuals.

The data may come from various sources. Sources of data may include datafrom a wearable body monitor, from sensors/transducers, fromcommunications technologies, from data integration technologies, fromsoftware services (such as feeds and web services), from metadata, frommanual entry, from user input, from user interfaces (such as frombuttons, dials, sliders, graphical user interfaces and the like), fromthird party sources, from databases, from surveys, from derived data,from records and transaction histories (such as library records, videorental records, media playlists, receipts, financial statements, creditcard statements, bank statements and the like) and the like. Data mayalso be obtained from non-invasive means and passive or indirect datagathering.

Data may be obtained from sensors and/or body monitors. A sensor or bodymonitor may have a specific shape or form, such as an arm band orgarment. A sensor or body monitor may be worn in specific locations,such as on the arm or around the waist. A sensor or body monitor may bewearable. Examples of body monitors other systems, devices, and methodsthat can be used to generate the data rendering life bits and ultimatelylifeotype data are described in described in Stivoric et al., U.S. Pat.No. 7,020,508, issued Mar. 28, 2006, entitled Apparatus for DetectingHuman Physiological and Contextual Information; Teller et al., pendingU.S. patent application Ser. No. 09/595,660, for System for MonitoringHealth, Wellness and Fitness; Teller, et al., pending U.S. patentapplication Ser. No. 09/923,181, for System for Monitoring Health,Wellness and Fitness; Teller et al., pending U.S. patent applicationSer. No. 10/682,759, for Apparatus for Detecting, Receiving, Derivingand Displaying Human Physiological and Contextual Information; Andre, etal., pending U.S. patent application Ser. No. 10/682,293, for Method andApparatus for Auto-Journaling of Continuous or Discrete Body StatesUtilizing Physiological and/or Contextual Parameters; Stivoric, et al.,pending U.S. patent application Ser. No. 10/940,889, Stivoric, et al.,pending U.S. patent application Ser. No. 10/940,214 for System forMonitoring and Managing Body Weight and Other Physiological ConditionsIncluding Iterative and Personalized Planning, Intervention andReporting, and Stivoric et al., pending U.S. patent application Ser. No.11/582,896 for Devices and Systems for Contextual andPhysiological-Based Detection, Monitoring, Reporting, Entertainment, andControl of Other Devices, each of which are incorporated, in theirentirety, herein by reference.

In an embodiment, the data may be obtained from an apparatus fordetecting, monitoring and reporting human status information, comprisinga sensor device including at least two sensors selected from the groupconsisting of physiological sensors and contextual sensors, said sensorseach capable of generating a data stream, wherein a first data streamcomprises data indicative of at least a first parameter and second datastream comprises data indicative of at least a second parameter of anindividual; and a computing device in electronic communication with saidsensor device, said computing device receiving at least a portion ofsaid data streams and generating derived data based on said dataindicative of at least a first parameter and said data indicative of atleast a second parameter, said derived data used to control saidcomputing device. In an embodiment, the data may be obtained from anapparatus for detecting, monitoring and reporting human statusinformation, comprising a sensor device including at least two sensorsselected from the group consisting of physiological sensors andcontextual sensors, said sensors each capable of generating a datastream, wherein a first data stream comprises data indicative of atleast a first parameter and second data stream comprises data indicativeof at least a second parameter of an individual; and a computing devicein electronic communication with said sensor device, said computingdevice receiving at least a portion of said data streams and generatingderived data based on said data indicative of at least a first parameterand said data indicative of at least a second parameter, said deriveddata used to control a device separate from said computing device.

Referring to FIG. 12A, the sensor or body monitor may be disposable,semi-durable or durable. The sensor or body monitor may be highlyintegrated, semi-integrated or disparate. In an embodiment, a sensor maybe highly integrated into a garment. The sensor or body monitor may benon-invasive, semi-invasive or invasive. The sensor or body monitor maybe implanted, wearable or proximal. The data may be obtained from onesensor, two sensors or more than two sensors.

The sensor or body monitor may be customized, proprietary oroff-the-shelf. The sensor or body monitor may be newly created, amodified existing sensor or body monitor or a previously existingsensor. The sensor or body monitor may be passive, active or acombination of passive and active. The sensor or body monitor may belocated in a housing, in communication with a housing or locatedremotely. The sensor or body monitor may be in remote communication witha central monitoring unit, in direct communication with a centralmonitoring unit or may be not related to a central monitoring unit. Thesensor or body monitor may be utilized in connection with a remoteprocessor, a local processor or without a processor. The sensor or bodymonitor may be automatic, user augmented, survey augmented or manual.

The sensor or body monitor may be direct, proximal or remote. The sensoror body monitor may be in body, on body or off body. The sensing of thesensor or body monitor may be proximal, physiological or contextual. Thesensor or body monitor may be located in a housing, in proximalcommunication with a housing or remote to a housing. The sensor or bodymonitor may be used in connection with linear algorithms, non-linearalgorithms, regression analysis and/or neural networks. The dataobtained from the sensor or body monitor may be raw data, direct data,modified data, heavily modified data or processed data. The data sensedby the sensor or body monitor may be physiological data, contextual dataand/or environmental data.

The sensor or body monitor may be implantable. An implantable sensor orbody monitor may be a pacing system, such as a heart pacemaker, cardiacpacemaker and the like. An implantable sensor or body monitor may be acarioverter defibulator. An implantable sensor or body monitor may be ablood pressure flow sensor, which may be MEMS-based. The sensor or bodymonitor may be a sleep apnea recorder, continuous positive air pressuredevice, ECG, Holter monitor, glucometer, pulse oximeter, blood pressuremonitor, sphygmomanometer, heart rate monitor, chest strap or the like.The sensor or body monitor may be disposable, such as a patch. Thesensor or body monitor may be capable of sensing physiologicalparameters such as glucose and other analytes contained in interstitialfluid. The sensor or body monitor may be may include chemical agents,electrotransport, ultrasound, microprojections, microneedles, analog ordigital weight scale and the like. The sensor or body monitor may be maybe included in fitness equipment such as cardio equipment, weighttraining equipment, scales, sports equipment, entertainment devices ingyms and the like. The sensor or body monitor may be included inconsumer electronics, such as MP3 players and phones. The sensor or bodymonitor may be included in entertainment devices, such as videogameconsoles. The sensor or body monitor may be included in GPS units. Thesensor or body monitor may be included in home appliances and homeautomation devices, which may control lighting, temperature, windowcoverings, security systems and access control, personal assistance,home theater and entertainment, phone systems and the like. The sensoror body monitor may be included in other device automation, such as acar, MP3 player and the like.

Referring to FIG. 13, data may be physiological data, contextual dataand/or environmental data. Physiological data may come directly from thebody and may be measured in a fairly direct fashion. In an embodiment,physiological data may be heart rate, respiration rate or whether anindividual is asleep or not asleep. Contextual data may include someconnotation of context. Contextual data may be a subset of environmentaldata, such as temperature near the body. Environmental data may includeinformation about the environment the body is in, such as ambienttemperature. The sensor or body monitor may be any one or morephysiological sensors, contextual sensors and/or environmental sensors.Other types of contextual, physiological and environmental data aredisclosed in pending U.S. patent application Ser. No. 11/582,896 forDevices and Systems for Contextual and Physiological-Based Detection,Monitoring, Reporting, Entertainment, and Control of Other Devices, eachof which are incorporated, in its entirety, herein by reference.

Referring back to FIG. 12A, the data sensed by the sensor or bodymonitor may be human status data, analytical status data orphysiological status data. The data may be not derived, may be derived,may be a derived third parameter or may be modified by a first or secondparameter. The data may be direct, compressed or filtered. The data maybe a surrogate or third parameter. The data sensed by the sensor or bodymonitor may be direct data, surrogate data or a combination of directand surrogate data. The data may be condition data. The condition may becomposed of a number of parameters and may be composed of a number ofconditions. The data obtained from the sensor or body monitor mayrelated to a body parameter, body condition and/or body state. Thesensor or body monitor may contain or be used in connection with an I/O,which may be on the sensor device or body monitor, proximal or inelectronic communication with the sensor device or body monitor orremote to the sensor device or body monitor. The output of the sensor orbody monitor may be or may form the basis for a report, index, trend orprediction. Feedback may be provided based on the data sensed by thesensor or body monitor. The feedback may be in the form of a list,coaching or behavior modification.

In an embodiment, the data may be obtained from a group of individualswaiting for heart transplants. The data may include medical values ofthe true declining cardiac output of the individuals. The data may alsoinclude changes in cardiac output or other body conditions whenindividuals are moved up or down the waiting list for a new heart. Thedata may include information regarding which individuals died before aheart was ready for them and the details of each death. This data mayrelate to life bit and life byte information (such as EE) to find a lifebyte that changes in a way that will allow for sorting of individuals onthe heart transplant waiting list to minimize deaths of people on thelist and to maximize the chances of survival after the operation, orother metrics of success.

In an embodiment, the data may include data relating to, or the platformmay analyze a subpopulation composed of, a group of individuals thathave some known and unusual outcome, conditions or situation. Forexample, the condition may be a rare mental disease, such as a splitpersonality. The platform may enable identification of one or more lifebytes that cluster this group; that is, separate them from the rest ofthe population. In an embodiment, the group may be individuals with MSand the life byte may be subtle but measurable changes in their activitylengths and patterns relative to their norms in the year just beforethey are diagnosed with MS.

In an embodiment, the platform may allow for identification of a groupof individuals that have some known and unusual life byte. The platformmay then be used to, or may itself, look for what outcomes or situationseach individual shares with others from this group. For example, theplatform may find that 0.1% of the population exercises more than 4hours a day every week and yet never exercises more than 1 day a week.The platform may identify characteristics that the people with thatlifebyte have in common. For example, the platform may identify thatthey all die before 60 years of age.

The platform may be used to conduct event studies and experiments. In anembodiment, the platform may be used to identify a group of individualsthat have a certain outcome or characteristic, such as, for example,high stress. The platform may also be used to identify certain otherevents or interventions that happened to certain subgroups of the groupof individuals. In this way that effects of the events or interventionscan be studied. As a result, the database can be used to determine theeffects of the intervention on the group of people, without additionalexperimentation. The platform may allow a user to form a hypothesis andthen examine or watch related groups of individuals in the database toconfirm or reject the hypothesis. The hypothesis may be modified overtime based on changes in the data, such as the subsequent effects of theevents and interventions of interest. The hypothesis may be reinforced,broken down and rebuilt. This may be an iterative process.

The platform may be used for predictions. In an embodiment, a user maydescribe or input their life bits, life bytes and other relevantinformation and the platform may determine lifeotypes or predict health,wealth, happiness outcomes and the like. The predictions may be based oninformation for individuals with similar life bits, life bytes,lifeotypes and related information. In another embodiment, the platformmay allow a user to explore the effects of certain changes on lifeotypesand outcomes. For example, the platform may allow a user to answer thefollowing question: if I changed my life bytes in this way, what shouldI expect in terms of changed health, wealth, happiness and the like?

The platform may enable maximization along certain dimensions. Referringto FIG. 26, in an embodiment, the platform may allow a user to “hillclimb” to the local maximum that seems like a reasonable set of changedlife bytes for a particular person such that it will maximize herhealth, wealth, happiness and the like. The user may be able to assignvarious weights to the various outcomes to indicate their relativeimportance to her. The platform may base the optimization, at least inpart, on data relating to other individuals, such as what is areasonable set of suggestible life byte changes for this person based onother similar people and whether or not similar people have been able tochange their lifeotypes in this way.

The platform may allow for comparisons. In an embodiment, the platformmay allow users to compare their life bit, life byte, lifeotype andother information and outcomes to other individuals or groups ofindividuals, such as similar individuals or groups of similarindividuals. In an embodiment, the platform may enable a one legged manin the deep South who sleeps poorly and is overweight to compare himselfto similar individuals, whether currently existing or based on pastdata, who are also trying to lose weight.

Life bits, life bytes, lifeotypes and/or related information may be usedto predict, determine or ascertain other characteristics or preferencesof a user or group of users. In an embodiment, fife bits, life bytes,lifeotypes and/or related information regarding a user's activity,activity, sleep patterns, body position and motoring times and lengthmay be used as the inputs to predict the movies or books or cars theuser will like.

The platform may allow for geospatial and visual presentation of lifebits, life bytes, lifeotypes and/or related information. In anembodiment, a Google-Earth style interface may be used to display lifebits, life bytes, lifeotypes and/or related information. The interfacemay show life bits, life bytes, lifeotypes and/or related informationfor a particular population or the entire world in a visually appealingand explorable way. In an embodiment, the platform may superimpose lifebits, life bytes, lifeotypes and/or related information over a 3D globeso that a user can see where people are awake, asleep, active,sedentary, stressed, calm and the like.

The platform and life bits, life bytes, lifeotypes and/or relatedinformation may be used for financial analysis and/or to predictinformation that is monetizable. In an embodiment, the platform, lifebits, life bytes, lifeotypes and/or related information may be used topredict changes in the stock market, or particular securities or groupsof securities, based on changes in life bits, life bytes, lifeotypesand/or related information. In an embodiment, the life bits, life bytes,lifeotypes and/or related information may be from around the country ora particular region. In an embodiment, the platform may aggregate thelife bits, life bytes, lifeotypes and/or related information intoindexes, such as a “people are getting sadder/pessimistic” and a “peopleare getting happier/optimistic” index. The platform may then use thoseindexes or indicators to predict near term and long term trends in theoverall market, or a subset of the market. In another embodiment, theplatform may enable prediction of individual stock trends from specificchanges in life bits, life bytes, lifeotypes and/or related information.For example, if people start jogging more, it may be advisable to stockin running shoe companies, such as Nike. If people start walking more itmay be advisable to buy more stock in Weight Watchers. In anotherembodiment, the platform, life bits, life bytes, lifeotypes and/orrelated information may be used to predict information relating tosporting events. For example, the information may be useful for bettingon sporting events. The platform may allow for aggregation ofinformation across many people connected to the sporting event.

The platform may be used for epidemiology applications. In anembodiment, the platform, life bits, life bytes, lifeotypes and/orrelated information may be used to predict the onset of a flu outbreakin a city 12 to 24 hours before it is otherwise seen by watching forsubtle shifting patterns in life bits, life bytes, lifeotypes and/orrelated information, such as higher estimated core temperature or loweractivity, adjusting for other relevant factors such as location, time ofday, weather patterns and the like. In another embodiment, the platformmay be used to identify patterns of behaviors, life bits, life bytes,lifeotypes and/or related information that lead to a certain outcome,such as a positive outcome. For example, sleeping 9 hours per night andexercising every day before noon may result in weight loss. In anembodiment, this information may be used to create a service business.

The platform may be used for data business applications. In anembodiment, access to life bits, life bytes, lifeotypes and/or relatedinformation may be sold or licensed. In an embodiment life bits, lifebytes, lifeotypes and/or related information may be sold. In anembodiment, a particular aggregate view of certain life bits, lifebytes, lifeotypes and/or related information may be sold to academicsfor the purpose of conducting outcome studies. This may allow thestudies to be performed on a much shorter time scale of a few minutes asopposed to several years. The platform may also allow for identificationof groups of interest. In an embodiment, the platform may allow foridentification of individuals with certain life bits, life bytes,lifeotypes and/or related information of interest. The platform mayenable a user to contact those people to seek additional information. Inembodiments, the people may be paid or given other consideration toprovide the missing or additional information. In an embodiment, theplatform may allow a user to identify a group of people who take aparticular pill, are of a particular ethnicity, and have a particularstress level. The user may want to know the fasting glucose level ofthese people, but that data is not available. The platform may enablethe user to, directly or indirectly, contact all or a portion of thesepeople, or one or more of their representatives, to obtain the fastingglucose level information. The people or their representatives may bepaid for the information. The newly obtained information may then beused in other applications.

The platform may be used for planning applications. In an embodiment,the platform may be used to automate budgeting and city planning. In anembodiment, instead of giving each state and city money based on howmuch the state or the American Automobile Association says the roads areutilized, life bits, life bytes, lifeotypes and/or related informationmay be used to make the determination. The determination may be made ona periodic basis, such as quarterly or annually, and the budgetadjusted. The platform may be used for similar applications in thehealthcare field. In an embodiment, the platform may utilize behavioralcensus information in connection with the determinations.

The platform may be used for social and social networking applications.In an embodiment, referring to FIG. 27, life bits, life bytes,lifeotypes and/or related information may be used for match making. Adating website or company may match people based on life bits, lifebytes, lifeotypes and/or related information. For example, a person whogoes to bed at 8 pm and wakes at 5 am is likely not to be compatiblewith someone who goes to be at 2 am regularly. In another embodiment,referring to FIG. 28, the platform may determine a user's probability oflocating a person with a particular lifeotype or range of lifeotypes ina particular location, such as a particular bar, neighborhood, city orcountry. For example, a dating website or business may use the platform,life bits, life bytes, lifeotypes and/or related information to assesswhether a particular city has compatible lifeotypes for a particularperson and if so in what quantities. This determination may be used toinforming vacationing and relocation decisions. For example, the personmay want to vacation in an area in which she has a high chance ofmeeting someone with a compatible lifeotype.

In an embodiment, a healthcare professional may summarize, or provideinformation, including life bits, life bytes, lifeotypes and/or relatedinformation, relating to, the types of patients she typically sees orthe types of patients she is good at seeing. This information may beaggregated with information obtained from patients, such as ratings,reviews, life bits, life bytes, lifeotypes and/or related information.The platform may enable a user, such as a patient, to choose ahealthcare provider based on this information. In an embodiment, theplatform may allow a patient to choose or recommend to a patient acertain healthcare provider that is good at treating people with thesame lifeotype as the patient. The healthcare provider may be any of thehealthcare providers described herein, including a doctor, nurse,pharmacist, physical therapist, weight management specialist and thelike. The healthcare professional may also be a more general serviceprovider such as a personal trainer, yoga instructor or the like. Inanother embodiment, the healthcare professional may be a an institutionor organization, such as a hospital, university, health maintenanceorganization, dentist office and the like.

In certain embodiments, the platform may enable the study of how certainlife bits, life bytes and other information impact and/or effect theevolution of lifeotypes. This information may be used to impact oraffect lifeotypes. In an embodiment, the impact of a particulartelevision show on a group of lifeotypes over time may be studied.Watching the television show may form a segment of life byteinformation. The show may be a program about weight loss, such as acontest to lose weight named “The Biggest Loser.” It may be determinedthat watching the program aids individuals who are between 10 and 45pounds overweight with weight loss. It may also be determined thatwatching the program frustrates people who are more than 60 poundsoverweight. This information may be used to affect the relevant lifebytes and lifeotypes by showing the program or similar programs tocertain groups of people, determined based on life bits, life bytes,lifeotypes and/or related information. The process may be consensual,with each person consenting to participation in the program.

In an embodiment, the relationship between life bits, life bytes,lifeotypes and/or related information and teaching and learning may bedetermined. Life bits, life bytes, lifeotypes and/or related informationalong with the relationships to teaching and learning may be used toseparate students into groups subject to different teaching techniquesto alter the efficacy of the teaching. In an embodiment, life bits, lifebytes, lifeotypes and/or related information may be used to alter oroptimize a method, system, process, work flow, organizational structure,structure, organization and the like. Life bits, life bytes, lifeotypesand/or related information collected from different people involved inor at different points in the method, system, process, work flow,organizational structure, structure, organization and the like may beused to alter or optimize the method, system, process, work flow,organizational structure, structure, organization and the like. In anembodiment, elderly people and the staff at an assisted living facilitymay be wearing body monitors. Using the monitors it may be possible todetermine when an elderly person soils his or her diaper and thisinformation may be collected and aggregates across all of the elderlypeople. Using the monitors, or by other means, it may be possible todetermine the frequency with which the staff changes the soiled diapers.For example, it may be determined that the staff make rounds to changediapers twice per day. The two patterns may be brought together toassess the typical delay between soiling and changing of a diaper andpossibly improve the situation by altering the pattern and reducing thedelay.

In an embodiment, life bits, life bytes, lifeotypes and/or relatedinformation may be used to tailor the delivery of advertising. Forexample, a person with a physically fit lifeotype that spends timebiking, may have bicycle ads focused at them. In another example, if twowomen always go walking together they may be good candidates for awomen's only gym, such as Curves. In an embodiment, life bits, lifebytes, lifeotypes and/or related information may be used for careercounseling. Life bits, life bytes, lifeotypes and/or related informationmay be collected in relation to various jobs and careers. Informationconcerning the satisfaction, ability, performance, happiness and thelike of people in certain professions may be collected and linked tolife bits, life bytes, lifeotypes and/or related information. Thisinformation may be used to generate norms or profiles of certainprofession and lifeotypes pairs or groupings which may be used forcareer counseling. In an embodiment, the platform may allow a user todetermine which job she should accept in order to maximize her happinessand productivity.

In embodiments, life bits, life bytes, lifeotypes and/or relatedinformation may be used to model or study transmission of certaindiseases and conditions. In an particular embodiment, life bits, lifebytes, lifeotypes and/or related information from many people in aparticular area may be used to build more detailed models of thetransmission of particular disease or condition whose onset isdetectable in the life bits, life bytes, lifeotypes and/or relatedinformation of the people. The disease or condition may be a cold, flu,infection or the like.

In embodiments, lifeotype information may be used for recruiting. In anembodiment, a company may determine use life bits, life bytes,lifeotypes and/or related information to determine that people withcertain lifeotypes function better at the company and may use thisinformation to inform hiring decisions. In embodiments, a company mayuse life bits, life bytes, lifeotypes and/or related information tobuild models of the kinds of lifeotypes that seem to drive retention andsuccess at work in order to try to promote those lifeotypes in thecompany. For example, if it turns out that people who sleep more than 8hours per day tend not to ever be promoted at a particular company, butthose who sleep less than 6 hours per night tend to burn out and quit,and those who fall in the middle stay at the company 90% of the timefrom year to year and the promotion rate is 35% from year to year, thenthe company may suggest or require time in bed to be changed from 7 to 9hours to 6 to 8 hours. In embodiments, life bits, life bytes, lifeotypesand/or related information may be used to monitor and affect morale in aworkplace, school, military environment, prison or the like.

Referring to FIGS. 14 through 17, lifeotypes may be identified andanalyzed in a variety of ways. The Platform may identify, generate andcreate lifeotypes. The analysis layer may identify, generate and createlifeotypes. The following techniques may be used to identify and analyzelifeotypes: iterative optimization, genetic programming, stochasticsimulations, model generation and model use (including dynamicprobabilistic networks), simulated annealing, Markov methods,reinforcement learning, partial programming, stochastic beam search,model based search, goal-based search, goal-based methods, feedbackloops and artificial intelligence. The Platform and/or analysis layermay learn. The Platform and/or the analysis layer may determine thenumber of life bits and life bytes to include in a lifeotype. Thisdetermination may be based on many factors, such as user selection,optimization of data processing or the number of traits required toobtain uniqueness. Feedback loops may identify additional life bits andlife bytes, or recommendations for new life bits and life bytes to seekdata in connection with. The processes involved may be dynamic.

Identifying lifeotypes may involve identifying parameters that may besensed. This may largely be determined by what is available. Identifyinglifeotypes may involve identifying parameters that may be derived. Thiswill be determined at least in part by what is useful for otherapplications. Identifying lifeotypes may involve identifying patterns inthe derived data. In an embodiment, the pattern may be many nights oflow sleep as a pattern of “prolonged sleep deprivation.” In anembodiment, the pattern may be many exercise events per week beingcalled an “active person.” In an embodiment, the pattern may be morethan 4 hours of exercise per day being identified as an “exercisebulimic.” In an embodiment, the steps for identifying lifeotypes mayinvolve identifying what is it about the world that is desired to beunderstood or predicted. For example, information concerning prolongedsleep deprivation. The next step may be determining if there arepatterns in the derived data that can be discovered through humanintervention and description, automatic discovery by a computer or both.The relationship of the patterns in the derived data to the topic to beunderstood may then be assessed. If there is a strong relationship theanalysis may be sufficient. If there is not a strong relationship, theanalysis may involve determining if there are new derivable parametersthat would be of assistance. If the data is available these parametersmay be added and the steps repeated. If this data is not available itmay be requested or surrogates may be identified. If this can not bedone or the raw data is not available, then the question may be asked“what could be added to the raw data pool (i.e. what new parameterscould be directly sensed or calculated or gathered in some way) suchthat the analysis can be performed? If such a set of new raw values inwould help and could be gathered then either add them or do what ittakes (i.e. adding the sensors to new body monitors) so that at somepoint in the future the data will have these new values and the processcan be repeated.

In an embodiment, lifeotypes may be created, identified, discovered andthe like by a lifeotype discovery module. The lifeotype discovery modulemay utilize a novelty detector, for example, in the domain wherephysiological data is collected and a large body of such data exists formany individuals. Any variable that, for some subset of individuals, isstatistically outside the norms for the population could be of interest.In an embodiment, for a large dataset, an infinite number of featuresmay be defined of varying complexity. This continuum can be thought ofas starting with single variable reports about an individual (e.g. theiraverage daily physical activity is low) to relative measures (e.g. theiraverage daily physical activity is low for their age) to complex patternbased interactions (e.g. their daily physical activity after a night ofpoor sleep is high for their age). The Platform may determine whichlifeotypes have utility. In one embodiment, the lifeotypes selected maybe those that have some predictive power with respect to otherlifeotypes, as determined by an analysis module.

In an embodiment, feature discovery may proceed by starting with thesimplest single variable features (e.g. total values per day of sleep,energy expenditure, or physical activity and the like) and examiningwhether statistically significant relationships exist to other measuresof interest (e.g. health outcomes, disease states, weight loss, stresslevel, and the like). The user may set up these different classes oflifeotypes (e.g. input and output) or the Platform may try all pairs. Inthis example, only features that are sufficiently strongly correlatedwould become true or saved lifeotypes. Another embodiment would utilizea random walk across pattern space (instead of using an ordered list),utilizing techniques from the stochastic beam search literature,evolutionary computation, simulated annealing, Markov Chain, Monte Carloand the like. The invention machine, in one embodiment, can beconstantly searching over the database to find relationships betweenpatterns and outcomes that exceed a given statistical level. A relatedembodiment allows the human users of the system to “prime” certainpatterns to be tested for first and/or serve as starting points for thesearch.

The Platform, analysis layer, sensors, systems and methods may becalibrated, such as by using algorithm to improve another. In anembodiment, a GSR measurement can be used to more correctly interpret aheart rate measurement. As a result, even in the absence of the GSRmeasurement, based on the past GSR data, a more accurate heart ratemeasurement may be obtained. This process may also allow for calibrationof slow changes in a user over time. For example, a user may wear moreclothing in the winter than in the summer. Calibration may be donethrough the use of a training pack and/or calibration pack. The trainingand/or calibration pack may be a component of an item of fitnessequipment. In an embodiment, the training pack may contain sensors whichmay measure heart rate. The data collected by the heart rate sensors maybe used to calibrate the algorithm used to determine energy expenditurefrom other sensors. The heart rate sensors may be more sensitive and acorrection algorithm may update or calibrate the determination of energyexpenditure. In an embodiment, a location pack may provide location andother contextual information, such as, in the car, in the wearer's homegym, and the like. The location and contextual data can be used tocalibrate the determination of energy expenditure. Contextual data mayalso be used to inform or adjust measurements and/or algorithms. Amarker may be used for calibration.

The Platform and/or analysis layer may analyze and process lifeotypesand related data. The Platform and/or analysis layer may identifylifeotype patterns and/or correlations across different populations,sub-populations, groups or sub-groups or across different lifeotypes,life bits and life bytes. The correlations may be overtime. The Platformand/or analysis layer may classify a population by sex, sexualorientation, race, ethnicity, culture, age, conditions, geographicregion, medical conditions, activity levels, participants in a certaingame or sport and the like. The Platform and/or analysis layer mayidentify relevant lifeotypes, life bits, life bytes, parameters, otherdata and the like. In an embodiment, the Platform and/or analysis layermay identify sub-populations in disparate sections of the world whichshare certain lifeotypes. For example, people in Helsinki and those in amountain valley region in California may share certain lifeotypes, lifebits and life bytes as they both live in a cloudy climate.

The Platform and/or analysis layer may identify pattern-inference pairsor groups. In an embodiment, the Platform and/or analysis layer mayidentify that a person who does X dies within Y or a person who doesactivity V is likely to contract condition W. The pattern-interferencepairs may take into account time and/or geography. The Platform mayallow for predictions of the future or identification and extension oftrends. The Platform may allow a user to determine how making a changein the past would affect a current situation. The Platform and/oranalysis layer may allow for self-testing. Platform and/or analysislayer may predict future outcomes for an individual and show likelydefault outcomes given current lifeotype expression. The Platform and/oranalysis layer may allow what-if testing. The Platform and/or analysislayer may utilize probabilities in the prediction of the future. Forexample, stopping smoking decreases chances of throat cancer andincreases the chances of short-term stress.

The Platform and/or analysis layer may generate many correlations,conclusions, results, pairs and the like and create a database of themwhich may be analyzed by the Platform and/or the analysis layer. ThePlatform and/or analysis layer may publish reports and suggest futurestudies. Platform and/or analysis layer may make recommendations. ThePlatform and/or analysis layer may generate treatment programs. ThePlatform and/or analysis layer may generate sub-populations orsub-groups for certain purposes. The Platform and/or analysis layer mayderive data. The Platform and/or analysis layer may utilize iterativeoptimization. The Platform and/or analysis layer may utilize geneticprogramming. The Platform and/or analysis layer may utilize feedbackloops. The Platform and/or analysis layer may utilize cycling back. ThePlatform and/or analysis layer may utilize artificial intelligence. ThePlatform and/or analysis layer may actively search for more information.The Platform and/or analysis layer may make requests of its users. In anembodiment the Platform and/or analysis layer may ask a user to providethree more blood samples.

The Platform and/or analysis layer may be mined as an invention machine.The Platform and/or analysis layer may utilize the concepts of aninvention machine, such as by being a goal-driven iterative enginesearching for solutions. The Platform and/or analysis layer may identifytrends in lifeotypes and in information accessed or provided to users.The Platform and/or analysis layer may use a loop to identify additionallife bits and life bytes, or recommendations for new life bits and lifebytes to seek data in connection with. The Platform and/or analysislayer may discover new life bits, life bytes, derived data, surrogatesand the like. The Platform and/or analysis layer may be used forpredicting. In an embodiment the Platform and/or analysis layer may beused to predict the success of research programs, success of projects,success of business initiatives, future disease states, stocks to buyand the like. The Platform and/or analysis layer may be used for guidedinformation gathering. Further, the Platform will reveal new types ofinformation that allow for the creation of particular assessment timesand protocols. For instance, it may be determined that viewing thecontinuous sensed data of an individual for 15 minutes upon waking willgive insight into whether that person is at risk for heart disease. Inthis way, the Platform can make specific predictions about individualsfrom specific sources and types of data, which the Platform itself hasdetermined to be optimal.

In an embodiment, the analysis may include identifying high valuelifeotypes. The Platform may examine a library of lifeotypes as a modelof the world with probabilistic outcomes and perform behavior learningusing any of a number of techniques to produce an optimal strategy toobtain a desired outcome. As an example, for a particular individual(say, a 35-year old smoker who also exercises vigorously three times aweek and eats poorly), the system may analyze the particular lifeotypeand determine that the most useful (and likely to be successful)strategy would be to cut back on smoking by 50% and eat better, ratherthan quitting smoking entirely. The system may determine this byconsidering many different action-strategies, using the stored data tosimulate the effects, and searching over the action space to find anoptimal policy. Reinforcement learning and the class of program searchstrategies may also allow the solution of this behavior optimizationstrategy.

Lifeotypes may be based on relative measures. There may be relativelifeotypes, relative life bytes and relative life bits. Changes from abaseline or norm may be recorded in connection with a relativelifeotype, relative life byte and/or relative life bit. Lifeotypes,including relative lifeotypes, may map to a diagnostic measure, such asnon-invasive glucose, pulse pressure from heat flux, skin temp, galvanicskin response and the like. The Platform may assist with understandingthe lifeotype associated with a particular life byte sequence, set oflife bytes and/or set of life bits. The Platform may also assist withdetermining the life byte sequence, set of life bytes and/or set of lifebits associated with a particular lifeotype. This process may beanalogous in certain respects to the protein folding problem. ThePlatform and/or analysis layer may utilize successive measures (e.g. oneweek recordings 4 times a year) to detect early the signs of a disease,such as heart disease. Coaching and/or human input may be part of theanalysis. The Platform and/or analysis layer may view or provide viewsof slices and/or aggregations of the data. This may generate automaticand accurate population models. The Platform and/or analysis layer mayutilize and/or contain databases, disk-based databases, distributeddatabases, store and forward databases, peer to peer databases and thelike.

Many different types of users or groups of users may use the Platformand/or lifeotypes and related concepts. These users or groups of usersmay be consumers of the Platform and/or lifeotypes and relatedinformation. A user may be a medical or scientific user, such as ascientist, researcher, doctor, healthcare professional, healthcareworker, caregiver, academic, educational institution, institution,hospital, other healthcare facilities, patient, an infant, a child, anadolescent, an adult, an elderly person and the like.

A user may be a lifestyle user, such as an athlete, personal trainer,gym, fitness club, sports team, youth group and the like. A user may bean entertainment user, such as a gamer, celebrity, fan and the like. Auser may be a business user, such as a marketer, advertiser, insurer,actuary, personnel in a health maintenance organization, data business,enterprise software business, financial services business, securitybusiness, investment industry business, an administrative user and thelike. A user may be someone who is curious. A user may be a policymaker, public health official, epidemiologist, government and the like.A user may be the World Health Organization, National Institutes ofHealth and the like. A user may be a consumer, employer, workplace,employee and the like. A user may be a community, social network and thelike. A user may also be an entity, such as a company, or a computersystem, such as a computer system that is making use of the Platform. Auser may be a system or method that is making use of the Platform and/orlifeotypes or related information.

The Platform may be applied in many ways including for medicalapplications, filtering data, publishing, report generation, policymaking, insurance-related applications, search, self-assessment,entertainment, applications relating to interactive spaces, novelty,controlling a device, operating a device, controlling a third parameter,monitoring a workplace, security, marketing, advertising, humanresources, military uses, law enforcement, first responders, sportsrecruiting, analytics, consulting, reviews, content presentation, dataintegration, data sales, reporting, concierge services, registries,royalty systems, artificial intelligence, sales, product design,therapy, advice, predictions, coaching, comparisons, financialapplications, e-commerce, voting, politics, crime scene investigation,forensics, identifying related persons, clinic trials, tagging and thelike. In discussing the application of the Platform, the term lifeotypemay also include lifebits, lifebytes and/or lifebyte sequences. Any ofthe applications of the Platform may be implemented as a system, method,apparatus, application and/or service.

The Platform may be utilized for medical applications, such as medicalmonitoring. In an embodiment, the Platform may be used to monitorpatients. The patients in an emergency room or in the waiting room ofthe emergency room may be outfitted with wearable monitors. Using themonitors, various lifeotypes of the patients can be ascertained. Thisinformation may be used for treatment. Healthcare providers can alsomonitor changes in the lifeotypes of patients and treat them before theycrash. Using the Platform and lifeotype information a healthcareprovider may be able to predict when a patient is going to crash andtreat the patient before that time. The Platform may be integrated withexisting monitoring systems in the emergency room and display lifeotype,life bits, life bytes and lifestyle data along side traditionalmonitoring systems. The Platform may be used in triage situations.Dynamic or low resolutions lifeotypes, as discussed herein, may be morerelevant in medical emergency or triage conditions. In an embodiment,the monitoring method may involve determining a condition of a body,comprising continuously measuring the pulse of the body; continuouslymeasuring the heat flux from the body; inferring from the measurementsof the pulse and the heat flux the nature of an activity of the body;and delivering information about the condition of the body that dependson the nature of the activity. In an embodiment, the monitoring may bein connection with a monitoring device, such as a sensor device,metabolic halter and the like. The data may be provided to a healthcareprofessional who may use the data in connection with a patentappointment, such as for a physical. In an embodiment, the data mayinclude data regarding energy expenditure, glucose levels and the like.In an embodiment, the data may be used in connection with monitoring andmanaging diabetes.

In an embodiment, the monitoring may be in connection with a medicaltrial, such as a pharmaceutical trial or the like. The monitoring mayfacilitate the collection of data and may result in the collection of awider and deeper range of data and data that is more objective than dataobtained by traditional means. The monitoring device may measuremetabolism and data concerning metabolic rate changes may be collected.In another example, the monitoring device may measure energyexpenditure, heart rate and galvanic skin response and also included aglucometer and accelerometer. The sensors may be non-invasive. The datamay be used in connection with diabetes and an algorithm may determinerelative levels of glucose based on the data. The glucose levels may becompared to energy expenditure levels to detect any inconsistencies. Thedata may be collected over time. The result may be the ability to trackrelative levels of glucose and alert an individual when necessary. In anembodiment, the systems and methods may be used in an intensive careunit to track VO2 and energy expenditure. In an embodiment, the systemsand method may be used to assess whether patients are receiving adequatenutrients. For example, the systems and methods may be used to assesswhether patients in a hospital are being over or under fed. In anembodiment, the systems and methods may be used in connection with hearttransplant patients to measure the strength of the heart overtime. In anembodiment the systems and methods may be used to measure energyexpenditure in connection with fiber maloma or fibromyalgia. In anembodiment, the systems and methods may be used to monitor or controldrug delivery. In an embodiment, energy expenditure and anotherparameter may be used to solve for a missing parameter or assess aninverse relationship on measured parameters. For example, energyexpenditure and weight may be used to solve for glucose and heart rate.In another example, with hypertension, a marker and energy expenditure,the systems and methods may be able to determine blood pressure. Inembodiments, the systems and methods may adapt, self-calibrate,calibrate based on past data, learn over time, reinforce learning andthe like.

In an embodiment, the Platform may facilitate determining an inverse,causation and/or cumulative relationship. In an example, a person it maybe determined that a person who has not slept in 36 hours and has noteaten in 10 hours, is likely to be fatigued. A cumulative condition maybe a condition where an individual's condition may be deduced from theindividual's behavior over some previous period of time. In anembodiment, techniques for determining an inverse, causation and/orcumulative relationship may be used by first-responders (e.g.firefighters, police, soldiers and the like). In an example, the wearerof a sensor device may be subject to extreme conditions and if heat fluxis too low for too long but skin temperature continues to rise, thewearer is likely to be in danger. In another embodiment, the inverse,causation and/or cumulative relationship may be determining why a babyis crying. The factors that may be considered include temperature, heartrate, orientation, activity type, state of sleep, crying and the like.In another embodiment, the inverse, causation and/or cumulativerelationship may be determining why a patient, such as a patient in anassisted living environment, is not getting well. In another embodiment,the inverse, causation and/or cumulative relationship may be determiningwhy a person in an emergency room is crashing. Factors that may beconsidered include sensor data, data from at least a two sensor array,hunger, temperature, fatigue and the like.

In a detailed embodiment, the Platform may be used to monitor certainparameters in connection with diabetes. The Platform may monitor energylevel and determine glucose levels and provide guidance. The Platformmay advise the patient, a doctor, healthcare provider or the like toadjust an insulin pump or to modify energy expenditure via lifestylechanges. The Platform may also consider markers, such as markersrelevant to type I diabetes, markers relevant to type II diabetes,genetic markers and the like. The Platform may also monitor weight,cardiac status, vascular effects, perfusion to periphery (such as feet),profusion of small blood vessels and the like. The Platform may alsomonitor surrogate measure or derive new surrogate measures. The Platformmay optimize inputs and outputs, such as by considering time relatedfactors.

The Platform may be utilized for medical decision making. In anembodiment, the Platform may be used to inform decisions regardingtreatment. Medical decisions can be based in whole or in part onlifeotypes and related data. The Platform may allow a user to plotlifeotypes against intraventions. Lifeotypes and related data can beused to assist medical professionals and patients with treatment choice.The Platform may enable identification of prior patients with similarlifeotypes and may enable review of the decision trees for thosepatients. In an embodiment, the Platform may track the decision tree ofa particular patient. In this regard, the Platform may help to predictthe outcome and likely effects of a treatment plan. The process may beautomated and the Platform may derive the advice. Using the Platform apatient may be able to determine which healthcare provider has the mostsuccessful treatment and/or rehabilitation record for the patient'slifeotype. Using the Platform the patient may be able to obtain userratings from other patients.

The Platform may be utilized for medical studies and/or diagnosis. ThePlatform may be used to better delineate known diseases, conditions andsyndromes and to identify new diseases, conditions and syndromes. ThePlatform may be used to identify new treatments. The Platform may beused for therapy. The Platform may be used to identify groups or cohortsfor therapy based on lifeotype. Support groups or clinical trial cohortsmay be created based on lifeotype. A patient may be paired or groupedwith other individuals who have or are dealing with similar issues orare in a similar state of health. A patient may be paired or groupedwith other individuals who have survived a particular condition ordisease or who have improved their condition. A user may connect withothers or review their data to determine what they did to achieve aparticular goal. The Platform may analyze and predict the likelihoodthat the therapy or treatment will work for another, using lifeotypedata.

The Platform may be used to determine the efficiency of medicalproviders. The Platform may be used to determine the efficiency of aparticular healthcare professional or of a department or functionalunit, such as an emergency room, nursing station, intensive care unit,laboratory, neonatal ward and the like. The Platform may be used todetermine and track the success rates and patient ratings of aparticular medical provider. The Platform may be used to track treatmentsuccess and patient ratings in general. The Platform may be used todeliver content based on lifeotypes and related information. In anembodiment, a patient may be provided with personalized healthcarecontent based on lifeotype and related data. In another embodiment, asearch may be customized based on lifeotype data. The Platform may allowfor the creation of content in real time. The Platform may generateblogs based on lifeotypes and related data. As discussed below, thecontent may be advertising.

The Platform may be utilized for disease management. The Platform mayperform lifeotype-based risk calculation in disease management toprevent or manage a disease, such as heart disease. The Platform may beused for drug titration. The Platform may, or enable a user to,preemptively identify disease treatment and prescribe treatment. In anembodiment, a person may have a hypertension-related lifeotype. ThePlatform may determine that exercise may benefit this person based onthe lifeotype information. The Platform may provide personalizedfeedback to the person. The Platform may generate a report. The Platformmay assist with modifying the behavior of the person. The Platform maygenerate a program guide and/or provide a program guide to the person.Based on exercise and nutrition, the Platform may predict bloodpressure, disease state, severity or changes in any of the foregoing.The relationship may be cause and effect or inverse/reverse diagnosis. Ahypertension marker may serve as a calibrator. Lifeotype information maybe used to inform drug delivery. The Platform may be applied towellness, health, diagnosis, condition management and the like.

With respect to choosing drugs and dosages, the data described hereinand changes to that data including lifeotype data could be used in muchthe same way as a persons genetic profile is used in pharmacogenomics.For example, an indicidual could be assessed with the systems anddevices described herein one time, or at intervals to determine thecorrect dosage.

In an embodiment, the Platform may be used in connection with thediagnosis of heart disease by providing a wearable body monitordisposable on the upper arm of a patient; deriving electrocardiogramfrom sensors associated with the wearable body monitor; comparing theelectrocardiogram with at least one electrocardiogram of a member of ahealthy population; and based on the comparison, making an assessment asto the probability that the patient has heart disease. In an embodiment,the Platform may be used in connection with managing stress by providinga wearable body monitor having at least two sensors for sensingconditions of the body; and deriving an indicator of stress from thedata streams of the two sensors.

In an embodiment, the Platform may be used in connection with supportingcare giving by providing a person with a wearable body monitor, themonitor including a plurality of sensors for sensing conditions of theperson's body; automatically inferring the nature of the activity of theperson from the output of the plurality sensors; and providing acaregiver for the person with information about the activity. In anembodiment, the Platform may be used in connection with therapeuticmethods by inferring a condition of the wearer of a wearable bodymonitor from the output of a plurality of sensors that are associatedwith the wearable body monitor; and based on the inferred condition,recommending a time for the administration of a therapy that is relatedto the inferred condition.

In an embodiment, the Platform may be used in connection with patchesand disposable sensors that may both sense body conditions and, in aclosed loop, possibly without human intervention, administer a therapywhich may change the body's state. In an embodiment, the Platform may beused in connection with an apparatus worn against the body with at leastone sensor, a processor, that senses the presence of a headache, andthat may administer a pain-relief medication through the skin. Thedetermination to administer the medication, determination of the doseand the like may consider lifeotype information. In an embodiment, thePlatform may be used in connection with an apparatus worn against thebody with at least one sensor, a processor that senses the imminence ofpanic-attack, and that administers a claming agent through the skin. Thedetermination to administer the agent, determination of the dose and thelike may consider lifeotype information. In an embodiment, the Platformmay be used in connection with an apparatus worn against the body withat least one sensor, a processor that senses the presence of stress andthat administers a tactile reminder to promote bio-feedback for stressreduction. The determination to administer the feedback, determinationof the duration and intensity of the feedback and the like may considerlifeotype information. In an embodiment, the Platform may be used inconnection with an apparatus worn against the body with at least onesensor, a processor that senses the presence of a heart attack or strokeand that administers a blood thinning medication through the skin. Thedetermination to administer the medication, determination of the doseand the like may consider lifeotype information.

In an embodiment, a marker may be used in connection with the Platformfor medical applications. The marker may be a marker related to the riskof lung cancer, such as consuming vegetables. The marker may be relatedto certain proteins and indicate information regarding exercise,diabetes, bone density and the like. The marker may be a genetic marker.The marker may take into account environmental factors. In anembodiment, a relevant marker may be identified and an individual may beprovided with a monitor. The monitor may collect information relevant tothe marker. The monitor may assist with administration of a program orregime. The monitor may assess compliance and adjust variables based onthe level of compliance. The data collected by the monitor may beprovided to a healthcare professional. The healthcare professional mayuse the data in connection with a physical. The data may indicate areduction in a condition. The data may be used to provide feedback or tocalibrate the system. The system and method may be used in connectionwith various conditions, such as diabetes, obesity and the like. In theaggregate the system and method may function as a health census for apopulation, group or nation and the like.

The Platform may include or function as a data filter. The Platform mayenable data to be sorted or viewed based on lifeotypes and related data.Using the Platform, it may be possible to obtain validated results in aparticular space for a particular lifeotype, even though that space wasnot tested directly. In an embodiment, a study on one topic may have hadmany results relevant to another topic, which is now relevant foranother purpose. Using the Platform, the data can be sorted and viewedbased on the other topic (with controls if necessary) and conclusionsmay be drawn about that topic. The Platform may facilitateauto-generation of control groups and datasets for appropriatecross-validation. Using the Platform, it may be possible to identify,based on lifeotype information, data sets that are a subset or crosssection of another data set obtained for a different purpose, that maybe relevant to other studies.

The Platform may be utilized for publishing. In an embodiment, thePlatform may auto-publish material based on lifeotypes. The material maybe reports, results, outcomes, studies and the like. In an embodiment, areport may be of the form of FIG. 29. In an embodiment, the Platform mayauto-complete forms, such as medical records, insurance forms and thelike. The Platform may publish to a doctor, patient, family, employer,insurer and the like. The Platform may suggest a revised treatment ordecision pattern. The Platform may include a publishing engine, whichmay auto-publish material. The publishing engine may make thedetermination to publish based on set parameters. In an embodiment, apatient may ask a question and if the results are interesting enoughthen the application may publish the response, such as in the form of ascientific paper, on the internet, making it available to other people.In an embodiment, the publication engine may publish material in thefollowing scenario: if 80% of patients with a particular lifeotypechoose option A, and 20% of patients with the same lifeotype chooseoption B, but option B actually produces better results. The publishingrule may be that when the outcome is counter intuitive, the publishingengine is to publish a paper automatically, provided that allcorrelations are above 0.9 and the sample size is 1000 or more people.The Platform and/or publication engine may utilize correlations,aggregation and statistics. The Platform and/or publication engine maypersonalize healthcare content based on lifeotype and related data. ThePlatform and/or publication engine may customize a search for a websitebased on lifeotype data. The Platform and/or publication engine maycreate blogs based on lifeotype and related data. The Platform and/orpublication engine may create a spatial map of lifeotypes, which may betied to location, emotions and other information.

The Platform may be utilized for policy making. In embodiments, thePlatform may be used to study problems and issues with a healthcaresystem, such as a country, state or provincial healthcare system. ThePlatform may be used to assist policy makers spending healthcarebudgets. The Platform may assist with determination of where to spendinsurance money. The Platform may be utilized for insurance-relatedapplications. Actuarial tables, probability tables and mortality tablesmay be based on lifeotypes. The Platform may be used in connection withinsurance sales. In embodiments, the Platform may assist withunderwriting insurance policies based on lifeotypes. The Platform mayassist with the determination of where to spend insurance money based onlifeotypes. Using the Platform, lifeotypes may be used to affectunderwriting, insurance pricing, annuity pricing, pricing of definedbenefit plans, benefits, determination of coverage, identification ofpre-existing conditions and the like. The Platform may form a part of aservice of associating lifeotypes with overall life expectancy or withinsured conditions.

The Platform may be utilized in connection with a search function.Lifeotypes may be used to filter, order and/or cluster search results.The search function may present content based on lifeotypes. The searchfunction may be based on a page rank style analysis of link structuresbased on lifeotypes. The search functionality may be a search enginewhich may account for lifeotype.

The Platform may be utilized for self-assessment. In an embodiment, thePlatform may recommend dietary decisions. The Platform may allow a userto review the success of different dietary plans for individuals withsimilar lifeotypes. The Platform may allow a user to compare the user'sown results on different plans. The Platform may allow a user to trackwhat is working for the user and for others based on lifeotype. ThePlatform may allow for consideration of an Atkins diet and may considerdata from a BodyBugg device. The Platform may allow a user to monitorfood intake and/or nutrition and assess effects based on lifeotype. ThePlatform may allow a user to monitor fitness and/or lifestyle choicesand assess effects based on lifeotype. The Platform may enable behaviormodification based on lifeotype. The Platform may assist a user intraining for a goal. The Platform may affect or maximize a user'ssuccess with respect to any project. In an embodiment, the Platform mayassist a user with a dietary regimen by deriving an indication of acalories consumed from the output of a wearable sensing device thatincludes a pulse meter and a heat flux meter; and wirelessly sendinginformation about calories consumed to a personal digital assistant ofthe patient. In an embodiment, the Platform may assess fitness byproviding a wearable body monitor having a pulse sensor and a heat fluxsensor; deriving an activity type from the outputs of the pulse sensorand the heat flux sensor; and based on the activity type and theoutputs, assessing the fitness level of the wearer.

The Platform may be utilized for entertainment-related applications.Social networking may be organized by lifeotype. In an embodiment, asocial networking website, such as myspace.com, may present content andfacilitate social networking or create groups based on lifeotype.Internet audio and video, such as on Youtube.com or Break.com, may beorganized or indexed and/or presented based on lifeotype. Lifeotypes maybe used as an index for content, media, entertainment, leisure and thelike. The Platform may be used to unite people based on lifeotypes. ThePlatform may be used for dating applications. Dates may be arranged orintroductions may be made based on lifeotype information. The Platformmay be used for competition. The Platform may identify groups ofcompetitors based on lifeotypes. The Platform may allow for theoperation of a device, such as an entertainment device, based onlifeotype. In certain embodiments, lifeotypes may be used as tags. Inother embodiments, tags may be interpreted based on lifeotypes.

The Platform may be used for gaming. In an embodiment, lifeotypes may beused in connection with holodeck type applications. In an embodiment,lifeotypes may be used in connection with massively multiplayer games. Aplayer's character(s) in a game, such as an online, multiplayer or othergame, may be affected by the player's lifeotype and actions in the realworld. In this way, lifeotypes and related information may restrict,enable or define the character(s). If a player becomes more fit, hischaracter(s) in the game may be able to run faster and jump higher. If aplayer improves his diet his character(s) may become stronger. If aplayer's lifeotype changes, similar changes may happen to thischaracter(s) in the game. In an embodiment, the Platform may provide abehavior feedback and/or modification program, with virtual or realcoaching, to guide an individual towards his character in a game, suchas a video game. The Platform may tailor experiences to a user. In anembodiment, the Platform may tailor the game and/or experience to theuser based on lifeotypes and related information. In an embodiment, auser may wear an armband for a week and the system may gather data andcalibrate the experience based on the information collected. ThePlatform may also allow a user to replay experiences of others. In anembodiment, the Platform may also enable the virtual courtship ofonline-sex-partners. In order to win the affections of someone online, auser may be required to “deserve” them in the real-world. Thisapplication may be an extension of an adult friend finder application.

The Platform and lifeotype information may be used for entertainmentwith interactive spaces as discussed herein. The Platform and lifeotypeinformation may be used for sports-related application. The participantsin a sports or gaming league may be chosen based on lifeotypes andrelated data. The teams for a sport or game may be chosen based onlifeotypes and related data. Other cohorts or groupings may be chosenbased on lifeotypes and related data. Lifeotypes and related data may beused to tag entertainment content by lifeotype. Lifeotypes and relateddata may be used to censor or scale content. In an embodiment, anindividual may be shown a less stressful version of a movie as a resultof this lifeotype. For example, his lifeotype may be characterized by aweak circulatory system and a pre-disposition toward heart attacks.Content may be delivered based on lifeotypes and related data. Contentmay be print media, such as books, news and the like, along with onlineanalogs. Content may also be audio, music, video, games, video games,blogs, podcasts, images, art, fine art and the like.

Lifeotypes and related information may be used in connection with or tocreate interactive spaces. A space may be affected based on thecombination of lifeotypes in the space and the proximity of certainlifeotypes. Lifeotypes may function as a filter that affects a certainspace or environment. Attributes or features of a space may be modifiedbased on lifeotypes or changes in lifeotypes. Variables of a space whichmay be modified include brightness, color, volume, sounds, temperature,air quality, pressure, distance between objects (such as furniture),protection from outside, status of entries, status of exits, presence ofobjects, absence of objects and the like. In an embodiment, the lightsin a room or section of a room may be dimmed when a person with alifeotype including susceptibility to migraines enters the room orsection of the room. In another embodiment, the space may be a buffet ina cafeteria. The buffet may re-configure the food offerings to presentsugar free food choices to a person with a diabetic lifeotype. Inanother example, the lights in a space may be dimmed and music may beplayed or modified if two compatible lifeotypes enter a space. In anembodiment, users may be equipped with stress meters and the space maybe a meeting room or auditorium and the Platform may provide feedback toa given user or others in the room.

Lifeotypes and related information may also be used for noveltypurposes. In an embodiment celebrity lifeotypes may be offered for saleor used for comparison purposes. Horoscopes may also be based onlifeotypes and related information. In another embodiment, thepopularity of lifeotypes may also be presented. A user may be able tosee how popular his lifeotype is and may be provided with a list offamous people with the same lifeotype or with compatible or antitheticlifeotypes. Lifeotypes may also be used to impact or control a device oranother parameter. In an embodiment, a sensor, processor, computingdevice or the like may be controlled based on lifeotype. A user may havea lifeotype for which the Platform determines another parameter shouldbe measured and the Platform may turn on another sensor to measure thatparameter. In an embodiment, lifeotypes and related information maytrigger an event or control of another device.

Lifeotypes and related information may be used for workplace monitoring.A workplace can be monitored or surveyed for lifeotypes and relatedinformation. In an embodiment, an employer may monitor employees, suchas by outfitting each employee with a wearable sensor device, todetermine when employees are stressed, and when a breakdown is likely tooccur, based on lifeotypes and related data. In another embodiment, themilitary may use lifeotype information to assess and monitor morale andidentify potential problems and issues. In embodiment, lifeotypeinformation may be used to assist with monitoring a worker by providinga wearable body monitor, the wearable body monitor including a pluralityof sensors and a facility for inferring the nature of the activity ofthe worker from the outputs of the sensors; and providing a reportgenerating facility for reporting the activities of the worker over aperiod of time. Lifeotypes and related information may also be appliedin security-related applications. Lifeotypes may be used to monitorprisoners, such as to predict a prison uprising. Lifeotypes may also beused to interpret the stress levels of border guards and security guardsto predict potential security breaches. Lifeotypes and the Platform maybe used for anti-terrorism applications. In another embodiment, theanxiety level of a truck driver, boxer and others may be monitored.

Lifeotypes and the Platform may be used for marketing and advertising.Marketing and advertising may be targeted based on lifeotypes andrelated information. Lifeotypes and related information can be combinedwith location and contextual data to further customize an advertisement.A marketer or advertiser may determine if a product works or is likelyto work for a target person or group based on lifeotypes and relateddata. Using the Platform and/or sensors or body monitors, a user canverify receipt of an advertisement or marketing message and alsodetermine the target person or group's response to the advertisement ormessage. In an embodiment, the Platform may permit a marketer todetermine if the target person laughed at the advertisement. Lifeotypesand related information may be self-reinforcing and may realize networkeffects. The more lifeotypes and related data that are generated themore valuable the Platform and the information becomes. Once there is abase of data for comparison and the like, more people will want to usethe Platform, systems and methods to take advantage of the data.

The Platform and lifeotype information may be used for recruitingpurposes. The Platform and lifeotype information may be used for humanresources related applications. In an embodiment, lifeotypes and relateddata may be used as part of the interview process, for recruiting,determining compensation, workforce management, performance evaluation,retirement planning, determining benefits, planning for succession andthe like. The Platform and lifeotypes may also be used in connectionwith recruiting for the military, law enforcement, fire fighting,paramedics, first responders and the like. The Platform and lifeotypesmay also be used to assess morale and for profiling and advancement.Lifeotypes and related information may be used to determine eligibilityfor certain ranks and missions. In an embodiment, the special forces mayhave certain lifeotype-related entrance criteria. The Platform andlifeotypes may also be used in sports recruiting. In embodiments, thePlatform and lifeotypes may also be used to locate and/or draftathletes.

Lifeotypes and related information may be purchased and sold. Anindividual may want to know his lifeotype or learn of changes in hislifeotype, and he may purchase this information. Individuals may alsosell their lifeotype information, such as to other individuals, thirdparties, data warehouses and the like. Lifeotypes may be sold withcomparative or interpretive information regarding lifeotypes in generalor specific lifeotypes. Lifeotypes may be sold with user manuals orother content regarding one or more lifeotypes. Analytics and consultingmay be provided in connection with lifeotypes. In embodiments, analyticsand consulting services may be provided in connection withidentification and analysis of lifeotypes. Lifeotypes and relatedinformation may also be used in connection with content presentation andcensoring. In an embodiment, a less intense version of a movie or amovie with an altered ending may be presented based on the lifeotype ofthe viewer. Reviews of content, products, services and the like may alsobe presented based on lifeotypes. Lifeotypes and related information maybe used to sort, filter and present reviews. In an embodiment, anaverage rating of a particular fitness product may be presented to auser, but the rating may consist of an average of only those ratingsfrom individuals with the same lifeotype as the user.

The Platform may be integrated with other systems that handle data. Theother systems may include medical systems, healthcare systems,entertainment systems, security systems, alarm systems, financialsystems, transactional systems, automobile systems, home networks, hometheatre systems, wireless networks, workplace information technologysystems, airport systems, airline systems, transportation environmentsystems, systems in recreational environments, such as sports arenas,concert halls and theatres, and the like. Lifeotype data and relateddata may be sold to data businesses. Lifeotype data and related data maybe used for data analysis, data mining, data warehousing and the like.In embodiments, a user may purchase a seat for use of the database. Inembodiments, a user may purchase analysis and services in connectionwith the data. In embodiments, users may purchase tailored datasets forstudies. Users may include researches, governments, health careorganizations, such as the World Health Organization, NationalInstitutes of Health, the Center for Disease Control and the like,academics, industry, private sector participants, commercial users,individuals and the like.

The Platform may include an artificial intelligence engine. Theartificial intelligence engine may utilize data or make use ofexperiences based on lifeotype data, such as by indexing informationbased on lifeotype. The Platform may generate reports, indexes,predictions and the like. The Platform may generate Dunn and Bradstreettype reports based on lifeotypes. The Dunn and Bradstreet type reportsmay relate to a company, users of a particular product, fans of aparticular show, fans of a particular sports team, audience and thelike. The Platform may allow for the identification of related personsbased on lifeotypes. Family trees may be built based on lifeotypeinformation. Lifeotypes may evolve overtime and across generations. ThePlatform may be used to study the evolution of lifeotypes. The evolutionof lifeotypes may be studied in relation to genetic evolutioninformation. Lifeotypes and related information may also be used incrime scene investigation and forensics. Lifeotype information may alsobe registered with a registry. In an embodiment, lifeotype informationfor criminals in a certain area may be registered with a lifeotyperegistry maintained by law enforcement.

Lifeotypes and related information may form part of a royalty system. Inan embodiment, a user may receive a payment if he or she chose to opt-into a lifeotype information sharing program. A person may receive aroyalty each time his lifeotype data is accessed. A person may receive aroyalty each time his lifeotype data is used in a study. A user mayparticipate in the royalty system on an anonymous basis. A user maychoose to opt-in or opt-out of an information sharing program. Thesystem may provide incentives for a user to opt-in.

Advertising may be targeted based on lifeotype. Bidding for ad placementmay be based on lifeotype. Lifeotype may be used as another demographic,psychographic or the like. Lifeotypes may be used as a way topersonalize ads. Lifeotypes and related information may be used for thetiming, placement and targeting of ads. In an embodiment, anadvertisement for an analgesic may be shown on a cell phone as a personis experiencing back ache. In another embodiment, the Platform mayidentify a person as experiencing arousal, then anger and thendepression, and delivery a Viagra advertisement to that person. Aloyalty or rewards program may be based on lifeotypes. The prizes forwhich points may be redeemed may be based on lifeotypes. Differentlifeotypes may receive different amounts of points as a reward for apurchase, action or the like. A sales pitch may be targeted based onlifeotypes and related information. The lifeotype profiles of customerset may be analyzed. Return on investment may be tied to lifeotype. Aproduct may be designed based on lifeotypes. In an embodiment, multipleversions of a product may be created based on lifeotype and versions forthe three most common lifeotypes may be produced.

Lifeotypes and related information may be used for therapy relatedapplications. Lifeotypes and related information may be used to targettherapy. Therapies may be tailored by lifeotype. The effects oftherapies may be assessed based on lifeotypes. The Platform maydetermine the efficacy of a therapy based on lifeotypes and relatedinformation. Recommendations and reviews may be based on lifeotypes andrelated information. Lifeotypes and related information may be used inconnection with the provision of advice. The delivery of advice may betailored based on lifeotypes. In an embodiment, an open-minded personmay receive advice with more recommendations than someone with a morestubborn lifeotype. The content of the advice may be tailored orfiltered based on lifeotype information. Marriage advice may be providedbased on lifeotypes and related information. Statistics of martialsuccess may be calculated based on lifeotypes and related information.The compatibility of spouses may be reviewed based on lifeotypeinformation. Career advice may be provided based on lifeotypes andrelated information. Recruiting and job seeking advice may be based onlifeotypes and related information.

Lifeotypes and related information may be used for generatingpredictions and coaching. In embodiments, a prediction may be of thestatus of a particular trait five years in the future and the predictionmay be based on lifeotypes. In embodiments, the coaching may be inconnection with a goal and/or an activity, such as a sport, hobby, foracademics and the like. Lifeotypes and related information may be usedfor comparisons. In an embodiment, the current status of a user may becompared to the status of the user at some time in the past. ThePlatform may analyze what a user was doing when he performed well in thepast and may make suggestions to return the user to his past performancestate or to improve on that state. The Platform may also determine whatlevel or status is typical for a user and may inform a user when he isback to normal. In an embodiment, the Platform may determine whether auser has returned to his normal state following an injury andrehabilitation. The Platform may enable comparisons to individuals whohave achieved a particular goal. In an embodiment, a basketball playermay be compared to Michael Jordan, in terms of lifeotype. The Platformmay generate a coaching strategy based on differences in lifeotypes. ThePlatform may calculate the probability that the basketball player willreach his goal, which may be playing as well as Michael Jordan. ThePlatform may provide feedback or behavior modification and may include acoaching engine. In an embodiment, coaching may be informed by one ormore guidance algorithms. A guidance algorithm may consider derivedand/or sensed data, a condition in connection with derived and/or senseddata, an environmental factor in connection with derived and/or senseddata and the like. In an embodiment, coaching may include guidance inrelation to diagnostic goals, prescriptive goals, alerts, reports,predictions and the like. In embodiment, the coaching engine and/or thePlatform may learn via learning algorithms considering data regarding anindividual, a population, genetics, evolution, neural nets and the like.

The Platform and lifeotypes and related information may be utilized forfinancial applications. In an embodiment, lifeotypes and relatedinformation may be used to assess principals and key economic people ina company. The Platform may aggregate lifeotype profiles acrosspopulations for analysis. The Platform may identify target markets,business prospects and the like based on lifeotype. The Platform andlifeotypes and related information may be utilized for e-commerceapplications. In embodiments, life bits may be obtained from e-commercetransactions. In embodiments, lifeotypes may be used in connection withe-commerce advertising, such as for targeted advertising and productplacement. In embodiments, auctions or reverse auctions may be catalogedbased on lifeotypes. Portals may also be based on lifeotypes and relatedinformation. In an embodiment, a portal may be tailored to a particularlifeotype or group of lifeotypes.

The Platform and lifeotypes and related information may be utilized forconcierge services. In an embodiment, the concierge service may be an“On Star” service based on lifeotypes and related information. Inembodiments, the concierge service interface may be wearable withservice based on lifeotypes. In an embodiment, the concierge service mayfunction as an assistant, guardian angel, protector and the like.Lifeotypes and related information may be included in a registry oflifeotype services. Voting and politics may be informed by lifeotypesand related information. Candidates may be assessed based on lifeotypesand related information. In an embodiment, a person of a particularlifeotype, such as a very active, outdoor oriented lifeotype, may bewell served by voting for a candidate with a similar lifeotype as thatperson may be more in tune with environmental issues that matter to theperson. Recommendations of which candidate to vote for may be generatedbased on lifeotypes and related information. The Platform may enableautomatic exclusions and/or incentives structures based on lifeotypes.In an embodiment, a user may not be able to drink, drive, eat in aparticular location and the like based on lifeotype. In an embodiment, auser may be provided with an incentive to eat at a particular location,such as a health food restaurant. Tax breaks may also be provided basedon lifeotypes, such as to encourage good, healthy, lawful and otherbehavior.

The Platform may include one or more user interfaces. The Platform mayinclude a user interface for input of data and selection of parametersand attributes. The Platform may include a user interface for viewingdata, processing data, viewing results and the like. The Platform mayinclude a user interface for mapping. Lifeotype information may besuperimposed on or presented using a map, such as Google Maps. In anembodiment, derived data may be placed on a map so that geographicclusters with similar characteristics or groups of individuals withsimilar lifeotypes may be located. The mapping may include an indicationof demographic and socioeconomic data. The mapping interface enablesvisualization of lifeotype data, identification of trends and thecombination of biology, motion and location.

The user interface may enable visualization of data and/or results. Thevisualization may be two-dimensional, three-dimensional,four-dimensional and/or multi-dimensional, including interactive-typespaces, methods, devices, and systems disclosed in Stivoric et al.,pending U.S. patent application Ser. No. 11/582,896 for Devices andSystems for Contextual and Physiological-Based Detection, Monitoring,Reporting, Entertainment, and Control of Other Devices, each of which isincorporated, in its entirety, herein by reference. The user interfacemay enable presentation of spatial representations of lifeotypes. Theuser interface may enable presentation of a web of inter-relatedlifeotypes. The user interface may enable presentation of a lifeotypealong with other data concerning the lifeotype. In an embodiment, theuser interface may display continuous physiological data relating tousers who have elected to opt-in to a data sharing program. Thecontinuous physiological data may be shared anonymously or openly. Partsof the continuous physiological data may be selectable. The continuousphysiological data may be queried through the user interface. Thequeries may be freeform, directed or suggested, including nearrelationship suggestions or hints. The query results may be weighted bytheir pertinences, popularity, likelihood of success or strength incorrelation.

The user interface may present lifeotypes and related information usingone or more spider map or the like. Referring to FIG. 18, a spider mapor the like may depict life bits, life bytes, lifeotypes and relatedinformation, along with relationships among the depicted items. Thespider map or the like may depict degrees of relevance andinter-relatedness in terms of color, size (as in FIG. 18), depth,distance (such as the distance between items and the degrees ofseparation of items) and the like. For a particular item, directlyrelated items may be linked to the item with a line, and other itemswith more degrees of separation may appear smaller, in a darker color,greyed out or the like. As a new item of interest is selected, thespider map or the like may re-center on that new item of interest. Theuser interface may allow filters and search parameters to be applied toa spider map or the like.

The user interface may also be used to highlight and explore certainfacts, such as facts that are already known to the user. A user may usethe Platform and/or interface to create a visualization of a factalready known to the user. The visualization may help the user tounderstand the fact and explore the relationship of that fact with otheritems of data.

In an embodiment, the Platform may determine a particular lifeotype fora particular user. A user may review the results in the context of apopulation in the user's area or in another area, such as bysuperimposing the results on a Google Earth type application. The usermay be able to identify clusters of people in the world with similarlifeotypes. For example, the user may determine that a cluster of peoplewith his lifeotype live in Pittsburgh and another cluster live in Oslo.The user interface may allow the user to superimpose other informationwhich may enable the user to identify other trends. For example, theinterface may allow the user to superimpose weather data, and the usermay determine that Pittsburgh and Oslo have similar sunlight andprecipitation patterns. The Platform may also suggest other relevant orexplanatory information. In an embodiment, the Platform may determinethat economic bracket is relevant and may display socioeconomic data onthe map in the background. The interface may allow for identification ofclusters of people with similar lifeotypes and related data, such assleeping six hours, similar body mass index and similar economicbrackets. The interface may also present near relationships, such as inthe form of a spider map or the like. Certain sections of the map may begreyed out or appear in the background. The interface may also suggestother related queries or bring other relevant information to theattention of the user. The interface may allow a user to comparelifeotypes and related information relevant to him or a person or groupof interest to norms, others individuals or groups, to the person orsubject himself or itself at another point in time, to subsets, tosubsets at other points in time. The interface may also allow for theaddition of constraints, restrictions, filters and the like, which maybe implicit, hidden or explicit.

The Platform may be implemented or provided using various architectures,systems and methods. FIGS. 19 through 23 depict several possibleembodiments of the Platform. The Platform may include or be implementedusing a server and/or server farm. The server may be a rackmount, tower,blade, desktop, portable, handheld and/or wearable server. The servermay be a uni-processor or multi-processor server. The server may form apart of a monolithic computer, cluster computer, distributed computer,super computer, shared computing environment or the like. The server maybe a Java, .NET or the like middleware server, such as for data storageand retrieval. The server may be characterized by offline learning andoptimization, such as through analysis, correlation, prediction and thelike.

The Platform may be composed of or contain various applications. Anapplication may be compiled or interpreted. An application may be astandalone application, an embedded application, a stored procedure(such as in a database), a library (which may be static or shared) andthe like. An application may be a server-side application or aclient-side application, such as Ajax. An application may be a mashup, awidget or the like. The Platform may be implemented using aservice-oriented architecture. At least one component, facility or layerof the Platform may be accessible as a service, such as a web service,and may be accessible from anywhere in the world. The service orientedarchitecture may be implemented using REST, RPC, DCOM, CORBA, WebServices, WSDL, BPEL, WS-CDL, WS-Coordination and the like.

The Platform may be implemented in a way compatible with or using a Web2.0 environment. The Platform may be implemented as a Web 2.0application. The Platform may include Web 2.0 applications. The Platformmay enable Web 2.0 applications that emphasize online collaboration andsharing among users. The Platform may be implemented using a network,such as any of the networks described herein. The Platform may be local,shared or a combination of the two. The Platform may be implementedusing a local network, a broad network or a combination of the two. ThePlatform may be local or fully distributed.

The Platform may be implemented using a three-tier (or n-tier)architecture. The architecture may include an application server, whichmay be a J2EE server (such as Tomcat, JOnAS, Servlet, JSP and the like)or may utilize CGI, mod_perl, ASP, .NET and the like. The architecturemay include a database server. The database may be a relationaldatabase, object database, stream database, flat database, networkdatabase, hierarchical database or the like. The Platform may include adatabase or database facility wherein data units are constructed torepresent time based representation of a plurality of derivedparameters, such as derived vital signs and the like. The data may beobtained from a body monitor, via data integration or the like. The datamay be obtained by a feed or pulled from sources. The data may beobtained by push and/or pull means. The database may be a distributeddatabase, federated database, online database, parallel database, realtime database, spatial database, statistical database, time seriesdatabase or the like. The network associated with the database may beone or more of the following network types: DAS, SAN, NAS, HSM, ILM,SAT, FAN and the like. The architecture may include a transactionprocessing management system.

The architecture may include a web server, such as Apache, IIS and thelike. The architecture may include one or more client-side applications.A client-side application may be a standalone application, widget,plug-in, in-browser script (such as Javascript) and the like. Thearchitecture may include a firewall. The firewall may be based on, orinclude functionality for, port forwarding, SPI, NAT, dynamic DNS, IPtunnel, VPN, DMZ and the like. The architecture may include a loadbalancer. Referring to FIG. 21, the architecture may be a round-robinDNS. Referring to FIG. 22, the architecture may be a cookie or URL-basedsession with software load balancer. Referring to FIG. 23, thearchitecture may be based on cookie-based sessions with a hardware loadbalancer. The architecture may include a switch, router, hub or thelike, which may be based on VLAN, LAN or the like.

The Platform may include a data mining repository, data warehouse or thelike. The Platform may include or make use of capabilities forextraction, transformation and loading of data. Referring to FIG. 6, thePlatform may include interfaces to other systems, applications andservices. An interface may be provided through an internet, extranet orthe like, such as by using CSU, DSU or the like. An interface may beprovided in a wired manner, such as through an Ethernet or the like, orin a wireless manner, such as through IrDA, free-space opticalcommunication, cellular, IEEE 802 or the like. An interface may beprovided through a personal area network, local area network,metropolitan area network, wide area network and the like. Referring toFIG. 20, interfaces may be provided to various systems and devices, suchas implantable monitors, medical treatment devices, disposable monitors,glucose monitors, pulse oximeters, blood pressure monitors, weightscales, heart rate monitors, fitness equipment, entertainment devices,home appliances, GPS devices, SenseWear armbands, personal computertablet PCs, PDAs, pagers, wireless email devices, Blackberries, Treos,smart phones, cellular phones, SenseWear companions, voice systems,telephony systems, VoIP systems, transcription systems, modems, highspeed internet access systems, third party monitors, internal servers,client servers, third party servers and the like.

The Platform may include data administration functionality. Referring toFIG. 6, the Platform may include security, logging, conditional accessand authentication functionality. The architecture may include securityfunctionality, such as conditional access, authentication, intrusiondetection and prevention and the like. The architecture may includelogging functionality. The architecture may include backup and recoveryfunctionality. The backup and recovery functionality may be enabledusing magnetic table, hard disk, optical disc, solid state storage andthe like. The backup and recovery functionality may be implementedon-line, off-line or a combination of the two. The backup and recoveryfunctionality may be provided offsite, remotely, onsite or in acombination. The architecture may include means for redundancy andfailover. Certain information or aspects of the Platform may berestricted to local use, while others may be fully shared.

The Platform may include data facilities. Data may be any of the datadescribed herein. Data may come from any of the sources describedherein. Data may be housed in databases, datamarts, data warehouses andthe like. The data may be directly supplied, such as directlydownloaded, may flow through the internet, may be distributed and thelike. Interfaces to data and data sources may include ODBC, JDBC and thelike.

The Platform may include a central monitoring unit. The Platform mayutilize a central monitoring unit, or the central monitoring unit mayimplement all or a portion of the Platform. The architecture of theplatform may enable data processing.

The elements depicted in flow charts and block diagrams throughout thefigures imply logical boundaries between the elements. However,according to software or hardware engineering practices, the depictedelements and the functions thereof may be implemented as parts of amonolithic software structure, as standalone software modules, or asmodules that employ external routines, code, services, and so forth, orany combination of these, and all such implementations are within thescope of the present disclosure. Thus, while the foregoing drawings anddescription set forth functional aspects of the disclosed systems, noparticular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context.

Similarly, it will be appreciated that the various steps identified anddescribed above may be varied, and that the order of steps may beadapted to particular applications of the techniques disclosed herein.All such variations and modifications are intended to fall within thescope of this disclosure. As such, the depiction and/or description ofan order for various steps should not be understood to require aparticular order of execution for those steps, unless required by aparticular application, or explicitly stated, or otherwise clear fromthe context.

The methods or processes described above, and steps thereof, may berealized in hardware, software, or any combination of these suitable fora particular application. The hardware may include a general-purposecomputer and/or dedicated computing device. The processes may berealized in one or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors or otherprogrammable devices, along with internal and/or external memory. Theprocesses may also, or instead, be embodied in an application specificintegrated circuit, a programmable gate array, programmable array logic,or any other device or combination of devices that may be configured toprocess electronic signals. It will further be appreciated that one ormore of the processes may be realized as computer executable codecreated using a structured programming language such as C, an objectoriented programming language such as C++, or any other high-level orlow-level programming language (including assembly languages, hardwaredescription languages, and database programming languages andtechnologies) that may be stored, compiled or interpreted to run on oneof the above devices, as well as heterogeneous combinations ofprocessors, processor architectures, or combinations of differenthardware and software.

Thus, in one aspect, each method described above and combinationsthereof may be embodied in computer executable code that, when executingon one or more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, means for performing thesteps associated with the processes described above may include any ofthe hardware and/or software described above. All such permutations andcombinations are intended to fall within the scope of the presentdisclosure.

While the invention has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present invention isnot to be limited by the foregoing examples, but is to be understood inthe broadest sense allowable by law.

All documents referenced herein are hereby incorporated by reference.

1. A method, comprising: assembling data from at least one data sourceinto at least one life bit; assembling the at least one life bit into atleast one life byte; and defining a lifeotype based on the attributes ofat least one life byte.
 2. The method of claim 1, wherein the datasources include sensed health data, residence data, medical records andsurvey data.
 3. The method of claim 1, wherein the life byte consists offour life bits, with one bit consisting of each of sensed health data,residence data, medical records and survey data. 4-5. (canceled)
 6. Themethod of claim 1, wherein the lifeotype is defined based on analysis ofcorrelation between attributes of a life byte and other informationabout a population of individuals. 7-14. (canceled)
 15. The method ofclaim 1, wherein more than one life byte is organized into a life bytesequence.
 16. The method of claim 1, wherein the at least one datasource is a body monitor including at least one sensor.
 17. The methodof claim 1, wherein the data is selected from the group consisting of:physiological data, contextual data and environmental data.
 18. Themethod of claim 1, wherein the data is selected from the groupconsisting of: derived data, analytical status data, contextual data,continuous data, discrete data, time series data, event data, raw data,processed data, metadata, third party data, physiological state data,psychological state data, survey data, medical data, genetic data,environmental data, transactional data, economic data, socioeconomicdata, demographic data, psychographic data, sensed data, continuouslymonitored data, manually entered data, inputted data, real-time date,feedback loop data, relative level data and changes in levels data. 19.The method of claim 1, wherein the data consists of at least two typesof data selected from the group consisting of: derived data, analyticalstatus data, contextual data, continuous data, discrete data, timeseries data, event data, raw data, processed data, metadata, third partydata, physiological state data, psychological state data, survey data,medical data, genetic data, environmental data, transactional data,economic data, socioeconomic data, demographic data, psychographic data,sensed data, continuously monitored data, manually entered data,inputted data, real-time date, feedback loop data, relative level dataand changes in levels data. 20-455. (canceled)
 456. The method of claim1, wherein the data is derived data, wherein said derived data isgenerated from both a first parameter and a second parameter of anindividual, said both first and second parameters being generated by asensor device and being at least one of a physiological, contextual andenvironmental parameter of said individual.
 457. (canceled)
 458. Themethod of claim 1, wherein the data is derived data generated by aprocessor from a first wearable sensor generating data indicative of atleast one of a first physiological, first contextual and firstenvironmental parameter of an individual and a second wearable sensorgenerating data indicative of at least one of a second physiological,second contextual and second environmental parameter of said individual,wherein the derived data is a status parameter that cannot be directlymeasured with any of said first and second sensors alone. 459-464.(canceled)
 465. A method, comprising: classifying a population ofindividuals into lifeotypes that correspond to defined sequences ofstatus data that are collected with respect to specified aspects of thehuman lifestyle.
 466. The method of claim 465, wherein the status datais selected from the group consisting of human status data, humancondition data, and human lifestyle data.
 467. (canceled)
 468. Themethod of claim 465, wherein the at least a subset of the status datawas obtained using a body monitor including at least one sensor. 469.The method of claim 465, wherein the status data is selected from thegroup consisting of: physiological data, contextual data andenvironmental data.
 470. The method of claim 465, wherein the statusdata is selected from the group consisting of: derived data, analyticalstatus data, contextual data, continuous data, discrete data, timeseries data, event data, raw data, processed data, metadata, third partydata, physiological state data, psychological state data, survey data,medical data, genetic data, environmental data, transactional data,economic data, socioeconomic data, demographic data, psychographic data,sensed data, continuously monitored data, manually entered data,inputted data, real-time date, feedback loop data, relative level dataand changes in levels data.
 471. The method of claim 465, wherein thestatus data consists of at least two types of data selected from thegroup consisting of: derived data, analytical status data, contextualdata, continuous data, discrete data, time series data, event data, rawdata, processed data, metadata, third party data, physiological statedata, psychological state data, survey data, medical data, genetic data,environmental data, transactional data, economic data, socioeconomicdata, demographic data, psychographic data, sensed data, continuouslymonitored data, manually entered data, inputted data, real-time date,feedback loop data, relative level data and changes in levels data.472-907. (canceled)
 908. The method of claim 465, wherein the statusdata is derived data, wherein said derived data is generated from both afirst parameter and a second parameter of an individual, said both firstand second parameters being generated by a sensor device and being atleast one of a physiological, contextual and environmental parameter ofsaid individual.
 909. (canceled)
 910. The method of claim 465, whereinthe status data is derived data generated by a processor from a firstwearable sensor generating data indicative of at least one of a firstphysiological, first contextual and first environmental parameter of anindividual and a second wearable sensor generating data indicative of atleast one of a second physiological, second contextual and secondenvironmental parameter of said individual, wherein the derived data isa status parameter that cannot be directly measured with any of saidfirst and second sensors alone. 911-915. (canceled)
 916. A method,comprising: classifying a population of individuals into lifeotypes thatcorrespond to defined sequences of status data, the status dataincluding data obtained by at least one sensor associated with theindividual.
 917. The method of claim 916, wherein the status data isselected from the group consisting of human status data, human conditiondata, and human lifestyle data.
 918. The method of claim 916, furthercomprising: analyzing patterns within and across lifeotypes to drawconclusions about individuals sharing a certain lifeotype.
 919. Themethod of claim 916, wherein the at least one sensor is associated witha body monitor.
 920. The method of claim 916, wherein the status data isselected from the group consisting of: physiological data, contextualdata and environmental data.
 921. The method of claim 916, wherein thestatus data is selected from the group consisting of: derived data,analytical status data, contextual data, continuous data, discrete data,time series data, event data, raw data, processed data, metadata, thirdparty data, physiological state data, psychological state data, surveydata, medical data, genetic data, environmental data, transactionaldata, economic data, socioeconomic data, demographic data, psychographicdata, sensed data, continuously monitored data, manually entered data,inputted data, real-time date, feedback loop data, relative level dataand changes in levels data.
 922. The method of claim 916, wherein thestatus data consists of at least two types of data selected from thegroup consisting of: derived data, analytical status data, contextualdata, continuous data, discrete data, time series data, event data, rawdata, processed data, metadata, third party data, physiological statedata, psychological state data, survey data, medical data, genetic data,environmental data, transactional data, economic data, socioeconomicdata, demographic data, psychographic data, sensed data, continuouslymonitored data, manually entered data, inputted data, real-time date,feedback loop data, relative level data and changes in levels data.923-1366. (canceled)
 1367. A method, comprising: classifying apopulation of individuals into lifeotypes that correspond to definedsequences of status data that are collected with respect to specifiedaspects of the human condition.
 1368. The method of claim 1367, whereinthe status data is selected from the group consisting of human statusdata, human condition data, and human lifestyle data.
 1369. The methodof claim 1367, further comprising: analyzing patterns within and acrosslifeotypes to draw conclusions about individuals sharing a certainlifeotype.
 1370. The method of claim 1367, wherein the at least a subsetof the status data was obtained using a body monitor including at leastone sensor.
 1371. The method of claim 1367, wherein the status data isselected from the group consisting of: physiological data, contextualdata and environmental data.
 1372. The method of claim 1367, wherein thestatus data is selected from the group consisting of: derived data,analytical status data, contextual data, continuous data, discrete data,time series data, event data, raw data, processed data, metadata, thirdparty data, physiological state data, psychological state data, surveydata, medical data, genetic data, environmental data, transactionaldata, economic data, socioeconomic data, demographic data, psychographicdata, sensed data, continuously monitored data, manually entered data,inputted data, real-time date, feedback loop data, relative level dataand changes in levels data.
 1373. The method of claim 1367, wherein thestatus data consists of at least two types of data selected from thegroup consisting of: derived data, analytical status data, contextualdata, continuous data, discrete data, time series data, event data, rawdata, processed data, metadata, third party data, physiological statedata, psychological state data, survey data, medical data, genetic data,environmental data, transactional data, economic data, socioeconomicdata, demographic data, psychographic data, sensed data, continuouslymonitored data, manually entered data, inputted data, real-time date,feedback loop data, relative level data and changes in levels data.1374-1811. (canceled)
 1812. The method of claim 1367, wherein the statusdata is derived data generated by a processor from a first wearablesensor generating data indicative of at least one of a firstphysiological, first contextual and first environmental parameter of anindividual and a second wearable sensor generating data indicative of atleast one of a second physiological, second contextual and secondenvironmental parameter of said individual, wherein the derived data isa status parameter that cannot be directly measured with any of saidfirst and second sensors alone. 1813-1817. (canceled)
 1818. In acomputer system, a computer-readable storage media storing: at least onecomputer program that operates to automatically generate a datastructure uniquely corresponding to an individual, wherein said datastructure comprises: a plurality of discrete data bits, said data bitscomprising at least one of information that is automatically sensedregarding the individual and information that is input into said system;and a plurality of bytes assembled from sequences of predefined types ofsaid discrete data bits, wherein said bytes are assembled into a datastructure unique to said individual.
 1819. The method of claim 1818,wherein the data bits include sensed health data, residence data,medical records and survey data.
 1820. The method of claim 1818, whereina byte consists of four bits, with one bit consisting of each of sensedhealth data, residence data, medical records and survey data. 1821-1823.(canceled)
 1824. The method of claim 1818, wherein a data bit isselected from the group consisting of: derived data, analytical statusdata, contextual data, continuous data, discrete data, time series data,event data, raw data, processed data, metadata, third party data,physiological state data, psychological state data, survey data, medicaldata, genetic data, environmental data, transactional data, economicdata, socioeconomic data, demographic data, psychographic data, senseddata, continuously monitored data, manually entered data, inputted data,real-time date, feedback loop data, relative level data and changes inlevels data.
 1825. The method of claim 1818, wherein a data bit consistsof at least two types of data selected from the group consisting of:derived data, analytical status data, contextual data, continuous data,discrete data, time series data, event data, raw data, processed data,metadata, third party data, physiological state data, psychologicalstate data, survey data, medical data, genetic data, environmental data,transactional data, economic data, socioeconomic data, demographic data,psychographic data, sensed data, continuously monitored data, manuallyentered data, inputted data, real-time date, feedback loop data,relative level data and changes in levels data. 1826-2270. (canceled)